20.7.3.9 Inspection -- Defect Detection, Crack Detection

Avalon Vision Solutions,
1991.
WWW Link. Vendor, Inspection. Industrial inspection systems.

Ejiri, M., Uno, T., Mese, M., Ikeda, T.,
A Process for Detecting Defects in Complicated Patterns,
CGIP(2), 1973, pp. 326-339. Expansion-contraction for thinning lines. BibRef 7300

Maitre, H.[Henri],
Defect recognition in numerical images by spectrum zero detection,
CGIP(5), No. 2, June 1976, pp. 238-244.
Elsevier DOI 0501
BibRef

Woods, P.W.[Peter W.], Allen, P.D.,
A cue generator for crack detection,
IVC(7), No. 4, November 1989, pp. 268-273.
Elsevier DOI 0401
BibRef

Gmytrasiewicz, P., Hassberger, J.A., Lee, J.C.,
Fault tree based diagnostics using fuzzy logic,
PAMI(12), No. 11, November 1990, pp. 1115-1119.
IEEE DOI 0401
BibRef

Leu, J.G.[Jia-Guu], Yau, H.L.[Hok-Lai],
Detecting the dislocations in metal crystals from microscopic images,
PR(24), No. 1, 1991, pp. 41-56.
Elsevier DOI 0401
BibRef

Perner, P.[Petra],
A Knowledge-Based Image-Inspection System for Automatic Defect Recognition, Classification, and Process Diagnosis,
MVA(7), No. 3, 1994, pp. 135-147. BibRef 9400
Earlier:
Application of knowledge-based image inspection system for diagnosis of misprints in offsetprinting,
CAIP93(738-749).
Springer DOI 9309

See also Case-Based Object Recognition with Application to Biological Images. BibRef

Bryson, N., Dixon, R.N., Hunter, J.J., Taylor, C.J.,
Contextual Classification of Cracks,
IVC(12), No. 3, April 1994, pp. 149-154.
Elsevier DOI BibRef 9404
Earlier: BMVC93(xx).
PDF File. BibRef

Azencott, R., Chalmond, B., Coldefy, F.,
Markov Fusion of a Pair of Noisy Images to Detect Intensity Valleys,
IJCV(16), No. 2, October 1995, pp. 135-145.
Springer DOI Cracks. Task is to detect defects using pairs of radiographic images. Defects are identified as intensity valleys. BibRef 9510

Kona, S.R.[Sudheer R.], Foster, J.W.[Joseph W.], Varughese, J.V.[Joseph V.],
A robust algorithm for detecting pinholes in transparent plastic films,
PR(26), No. 8, August 1993, pp. 1215-1227.
Elsevier DOI 0401
BibRef

Asaeda, T.[Teruo], Nousou, K.[Kazunori], Imanisi, M.[Masanori], Suzuki, Y.[Yutaka], Katabami, S.[Sachiyo],
Inspection system and process,
US_Patent5,734,742, Mar 31, 1998
WWW Link. BibRef 9803

Nesi, P.[Paolo], Trucco, E.[Emanuel],
Guest Editorial: Special Issue on Real-Time Defect Detection,
RealTimeImg(5), No. 1, February 1999, pp. 1-2. BibRef 9902

Zabih, R.[Ramin], Halviatti, R.[Ramin],
Method and apparatus for analyzing computer screens,
US_Patent6,226,407, May 1, 2001
WWW Link. BibRef 0105

Kazantsev, I.G., Lemahieu, I., Salov, G.I., Denys, R.,
Statistical detection of defects in radiographic images in nondestructive testing,
SP(82), No. 5, May 2002, pp. 791-801.
HTML Version. 0206
BibRef

Vachtesvanos, G.J.[George J.], Dorrity, L.J.[Lewis J.], Wang, P.[Peng], Echauz, J.[Javier], Mufti, M.[Muid],
Method and apparatus for analyzing an image to detect and identify patterns,
US_Patent6,650,779, Nov 18, 2003
WWW Link. BibRef 0311

Lam, E.Y.[Edmund Y.],
Robust minimization of lighting variation for real-time defect detection,
RealTimeImg(10), No. 6, December 2004, pp. 365-370.
Elsevier DOI 0501
BibRef

Akgul, Y.[Yusuf], Bachelder, I.A.[Ivan A.], Wagman, A.[Adam], Davis, J.[Jason], Koljonen, J.[Juha], Morje, P.[Prabhav],
Methods and apparatuses for detecting classifying and measuring spot defects in an image of an object,
US_Patent7,162,073, Jan 9, 2007
WWW Link. BibRef 0701

Ribeiro, B.,
Support vector machines for quality monitoring in a plastic injection molding process,
SMC-C(35), No. 3, August 2005, pp. 401-410.
IEEE DOI 0508
BibRef

Sakata, Y.[Yukinobu], Kaneko, S.[Shuni'chi], Takagi, Y.J.[Yu-Ji], Okuda, H.[Hirohito],
Successive pattern classification based on test feature classifier and its application to defect image classification,
PR(38), No. 11, November 2005, pp. 1847-1856.
Elsevier DOI 0509
BibRef

Sakata, Y.[Yukinobu], Kaneko, S.[Shuni'chi], Tanaka, T.,
Successive pattern learning based on test feature classifier and its application to dynamic recognition problems,
IEVM06(xx-yy).
PDF File. 0609
BibRef

Tsai, D.M.[Du-Ming], Yang, C.H.[Cheng-Hsiang],
A quantile-quantile plot based pattern matching for defect detection,
PRL(26), No. 13, 1 October 2005, pp. 1948-1962.
Elsevier DOI 0509
BibRef

Tsai, D.M.[Du-Ming], Yang, R.H.[Ron-Hwa],
An eigenvalue-based similarity measure and its application in defect detection,
IVC(23), No. 12, 1 November 2005, pp. 1094-1101.
Elsevier DOI 0510
BibRef

Liu, J.J.[J. Jay], MacGregor, J.F.[John F.],
Estimation and monitoring of product aesthetics: Application to manufacturing of 'engineered stone' countertops,
MVA(16), No. 6, 2006, pp. 374-383.
Springer DOI 0603
BibRef

Taniguchi, K.[Kazutaka], Ueta, K.[Kunio], Tatsumi, S.[Shoji],
A mura detection method,
PR(39), No. 6, June 2006, pp. 1044-1052.
Elsevier DOI 0604
Mura (irregular lightness variation on a manufactured surface); Defect; Detection; Vision; Spatial frequency; Contrast enhancement BibRef

Ng, H.F.[Hui-Fuang],
Automatic thresholding for defect detection,
PRL(27), No. 14, 15 October 2006, pp. 1644-1649.
Elsevier DOI 0609
Automatic thresholding; Defect detection BibRef

Tsap, L.V.[Leonid V.], Duchaineau, M.[Mark], Goldgof, D.B.[Dmitry B.], Shin, M.C.[Min C.],
Data-driven feature modeling, recognition and analysis in a discovery of supersonic cracks in multimillion-atom simulations,
PR(40), No. 9, September 2007, pp. 2400-2407.
Elsevier DOI 0705
Data-driven; Feature modeling (analysis, extraction, recognition); Image motion analysis; Physics-based; Supersonic cracks; Molecular dynamics; Atomic simulation; Nanoscale analysis BibRef

Caleb-Solly, P., Smith, J.E.,
Adaptive surface inspection via interactive evolution,
IVC(25), No. 7, 1 July 2007, pp. 1058-1072.
Elsevier DOI 0705
Machine vision; Configuration; Interative; Evolution User knowledge to get description for defects. BibRef

Groby, J.P.[Jean-Philippe], Lesselier, D.[Dominique],
Localization and characterization of simple defects in finite-sized photonic crystals,
JOSA-A(25), No. 1, January 2008, pp. 146-152.
WWW Link. 0801
BibRef

Lim, M.S.[Mee-Seub], Lim, J.H.[Joon-Hong],
Visual measurement of pile movements for the foundation work using a high-speed line-scan camera,
PR(41), No. 6, June 2008, pp. 2025-2033.
Elsevier DOI 0802
Construction. Line-scan image; Visual measurement; Pile movement BibRef

Choi, K.N.[Kyu Nam], Park, N.K.[No Kap], Yoo, S.I.[Suk In],
Image Restoration for Quantifying TFT-LCD Defect Levels,
IEICE(E91-D), No. 2, February 2008, pp. 322-329.
DOI Link 0802
BibRef

Barazzetti, L.[Luigi], Scaioni, M.[Marco],
Crack measurement: Development, testing and applications of an automatic image-based algorithm,
PandRS(64), No. 3, May 2009, pp. 285-296.
Elsevier DOI 0905
Metrology; Measurement; Matching; Close-range photogrammetry; Target recognition Extraction of cracks, measurement of crack deformations. BibRef

Liew, C.K.[Chin Kian], Veidt, M.[Martin],
Pattern recognition of guided waves for damage evaluation in bars,
PRL(30), No. 3, 1 February 2009, pp. 321-330.
Elsevier DOI 0804
Ultrasonics; Quantitative nondestructive evaluation; Structural health monitoring; Multi-layer perceptron; Ensemble networks; Modular networks BibRef

Gnanaprakasam, P.[Pradeep], Parker, J.M.[Johne M.], Ganapathiraman, S.[Subburengan], Hou, Z.[Zhen],
Efficient 3D characterization of raised topological defects in smooth specular coatings,
IVC(27), No. 4, 3 March 2009, pp. 319-330.
Elsevier DOI 0804
Surface reflectance model; Surface quality of specular coatings; Defect characterization; Camera calibration BibRef

Kim, H.I., Lee, S.H., Cho, N.I.,
Automatic Defect Classification Using Frequency and Spatial Features in a Boosting Scheme,
SPLetters(16), No. 5, May 2009, pp. 374-377.
IEEE DOI 0903
Uses histogram of spatial orientation and frequency. BibRef

Hampel, U., Maas, H.G.,
Cascaded image analysis for dynamic crack detection in material testing,
PandRS(64), No. 4, July 2009, pp. 345-350.
Elsevier DOI 0907
Image matching; Image analysis; Deformation measurement BibRef

Tsneg, Y.H.[Yan-Hsin], Tsai, D.M.[Du-Ming],
Defect detection of uneven brightness in low-contrast images using basis image representation,
PR(43), No. 3, March 2010, pp. 1129-1141.
Elsevier DOI 1001
Defect detection; Surface inspection; Basis image representation; Independent component analysis; Particle swarm optimization BibRef

Tsai, D.M.[Du-Ming], Tsai, H.Y.[Hsin-Yang],
Low-contrast surface inspection of mura defects in liquid crystal displays using optical flow-based motion analysis,
MVA(22), No. 4, July 2011, pp. 629-649.
WWW Link. 1107
BibRef

Burgess, G., Shortis, M.R., Scott, P.,
Photographic assessment of retroreflective film properties,
PandRS(66), No. 5, September 2011, pp. 743-750.
Elsevier DOI 1110
Retroreflective film; Retroreflectance; Luminance factor; Standards; Performance BibRef

Zou, Q.[Qin], Cao, Y.[Yu], Li, Q.Q.[Qing-Quan], Mao, Q.Z.[Qing-Zhou], Wang, S.[Song],
Automatic inpainting by removing fence-like structures in RGBD images,
MVA(25), No. 7, October 2014, pp. 1841-1858.
WWW Link. 1410
BibRef

Satorres Martínez, S., Gómez Ortega, J., Gámez García, J., Sánchez García, A.,
A machine vision system for defect characterization on transparent parts with non-plane surfaces,
MVA(23), No. 1, January 2012, pp. 1-13.
WWW Link. 1201
BibRef

Gunkel, C.[Christina], Stepper, A.[Alexander], Müller, A.C.[Arne C.], Müller, C.H.[Christine H.],
Micro crack detection with Dijkstra's shortest path algorithm,
MVA(23), No. 3, May 2012, pp. 589-601.
WWW Link. 1204
BibRef

Tolba, A.S.[Ahmad Said],
A novel multiscale-multidirectional autocorrelation approach for defect detection in homogeneous flat surfaces,
MVA(23), No. 4, July 2012, pp. 739-750.
WWW Link. 1206
BibRef

Wang, X.S.[Xiao-Song], Mirmehdi, M.[Majid],
Archive Film Defect Detection and Removal: An Automatic Restoration Framework,
IP(21), No. 8, August 2012, pp. 3757-3769.
IEEE DOI 1208
BibRef
Earlier:
Archive Film Restoration Based on Spatiotemporal Random Walks,
ECCV10(V: 478-491).
Springer DOI 1009
BibRef
Earlier:
HMM based Archive Film Defect Detection with Spatial and Temporal Constraints,
BMVC09(xx-yy).
PDF File. 0909
BibRef

Tsai, D.M.[Du-Ming], Chen, M.C.[Ming-Chun], Li, W.C.[Wei-Chen], Chiu, W.Y.[Wei-Yao],
A fast regularity measure for surface defect detection,
MVA(23), No. 5, September 2012, pp. 869-886.
WWW Link. 1208
BibRef

Jahanshahi, M.R.[Mohammad R.], Masri, S.F.[Sami F.], Padgett, C.W.[Curtis W.], Sukhatme, G.S.[Gaurav S.],
An innovative methodology for detection and quantification of cracks through incorporation of depth perception,
MVA(24), No. 2, February 2013, pp. 227-241.
WWW Link. 1302
BibRef

Tsai, Z.D.[Zong-Da], Perng, M.H.[Ming-Hwei],
Defect detection in periodic patterns using a multi-band-pass filter,
MVA(24), No. 3, April 2013, pp. 551-565.
WWW Link. 1303
BibRef

Li, G.H.[Guo-Hui], Shi, J.F.[Jin-Fang], Luo, H.S.[Hong-Sen], Tang, M.G.[Mian-Gang],
A computational model of vision attention for inspection of surface quality in production line,
MVA(24), No. 4, May 2013, pp. 835-844.
WWW Link. 1304
BibRef

Lu, H.T.[Hong-Tao], Huang, W.[Wei],
Automatic Defect Classification of TFT-LCD Panels with Shape, Histogram and Color Features,
IJIG(13), No. 03, 2013, pp. 1350011.
DOI Link 1309
BibRef

Anwar, S.[Said], Abdullah, M.[Mohd],
Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique,
JIVP(2014), No. 1, 2014, pp. 15.
DOI Link 1404
BibRef

Haddad, B.[Bashar], Jarrah, A.[Amin],
Semi-Automatic Cracks Correction Based on Seam Processing, Stochastic Analysis and Learning Process,
IJIG(13), No. 04, 2013, pp. 1350020.
DOI Link 1404
BibRef

Zhao, G.T.[Guo-Teng], Wang, T.Q.[Tong-Qing], Ye, J.Y.[Jun-Yong],
Anisotropic clustering on surfaces for crack extraction,
MVA(26), No. 5, July 2015, pp. 675-688.
WWW Link. 1506
BibRef

Hung, M.H.[Mao-Hsiung], Hsieh, C.H.[Chaur-Heh],
A novel algorithm for defect inspection of touch panels,
IVC(41), No. 1, 2015, pp. 11-25.
Elsevier DOI 1508
Automatic optical inspection BibRef

Bhattacharjee, S.[Sudipta], Deb, D.[Debasis],
Development of a crack growth predictor for geomaterials using detrended fluctuation analysis and optical flow method,
SIViP(10), No. 1, January 2016, pp. 121-128.
WWW Link. 1601
BibRef

Liebold, F., Maas, H.G.,
Advanced spatio-temporal filtering techniques for photogrammetric image sequence analysis in civil engineering material testing,
PandRS(111), No. 1, 2016, pp. 13-21.
Elsevier DOI 1601
Material testing BibRef

Weigl, E.[Eva], Heidl, W.[Wolfgang], Lughofer, E.[Edwin], Radauer, T.[Thomas], Eitzinger, C.[Christian],
On improving performance of surface inspection systems by online active learning and flexible classifier updates,
MVA(27), No. 1, January 2016, pp. 103-127.
Springer DOI 1601
BibRef

Chen, S.H.,
Inspecting lens collars for defects using discrete cosine transformation based on an image restoration scheme,
IET-IPR(10), No. 6, 2016, pp. 474-482.
DOI Link 1606
curve fitting BibRef

Ortner, T.[Thomas], Sorger, J.[Johannes], Piringer, H.[Harald], Hesina, G.[Gerd], Gröller, E.[Eduard],
Visual analytics and rendering for tunnel crack analysis,
VC(32), No. 6-8, June 2016, pp. 859-869.
Springer DOI 1608
BibRef

Dobson, J., Cawley, P.,
Independent Component Analysis for Improved Defect Detection in Guided Wave Monitoring,
PIEEE(104), No. 8, August 2016, pp. 1620-1631.
IEEE DOI 1608
Corrosion BibRef

Wang, H.C.[Hong-Cheng], Xiong, Z.Y.[Zi-You], Finn, A.M.[Alan M.], Chaudhry, Z.[Zaffir],
A context-driven approach to image-based crack detection,
MVA(27), No. 7, October 2016, pp. 1103-1114.
Springer DOI 1610
BibRef

Salucci, M.[Marco], Anselmi, N.[Nicola], Oliveri, G.[Giacomo], Calmon, P.[Pierre], Miorelli, R.[Roberto], Reboud, C.[Christophe], Massa, A.[Andrea],
Real-Time NDT-NDE Through an Innovative Adaptive Partial Least Squares SVR Inversion Approach,
GeoRS(54), No. 11, November 2016, pp. 6818-6832.
IEEE DOI 1610
NDT: Nondestructive testing. Current measurement BibRef

Krishnamoorthy, R.[Ramasamy], Ganesh, M.[Mani],
A simple computational framework for defect detection system with orthogonal polynomials transcoded coefficients,
JVCIR(41), No. 1, 2016, pp. 31-46.
Elsevier DOI 1612
Block classification BibRef

Hanzaei, S.H.[Saeed Hosseinzadeh], Afshar, A.[Ahmad], Barazandeh, F.[Farshad],
Automatic detection and classification of the ceramic tiles' surface defects,
PR(66), No. 1, 2017, pp. 174-189.
Elsevier DOI 1704
Ceramic tile BibRef

Abraham, R.[Romain], Bergounioux, M.[Maïtine], Debs, P.[Pierre],
Automatic Choice of the Threshold of a Grain Filter via Galton-Watson Trees: Application to Granite Cracks Detection,
JMIV(60), No. 1, January 2018, pp. 50-69.
Springer DOI 1801
BibRef

Lin, Z.[Zhe], Zhao, X.H.[Xiao-Hua],
Geometrical flow-guided fast beamlet transform for crack detection,
IET-IPR(12), No. 3, March 2018, pp. 382-388.
DOI Link 1802
BibRef

Gao, B., Li, X., Woo, W.L., Tian, G.Y.,
Physics-Based Image Segmentation Using First Order Statistical Properties and Genetic Algorithm for Inductive Thermography Imaging,
IP(27), No. 5, May 2018, pp. 2160-2175.
IEEE DOI 1804
crack detection, eddy current testing, feature extraction, genetic algorithms, image segmentation, infrared imaging, non-destructive testing and evaluation BibRef

Deng, X.J.[Xiao-Juan], Zuo, F.F.[Fei-Fei], Li, H.W.[Hong-Wei],
Cracks Detection Using Iterative Phase Congruency,
JMIV(60), No. 7, September 2018, pp. 1065-1080.
WWW Link. 1808
BibRef

Makaremi, M.[Mohammad], Razmjooy, N.[Navid], Ramezani, M.[Mehdi],
A new method for detecting texture defects based on modified local binary pattern,
SIViP(12), No. 7, October 2018, pp. 1395-1401.
WWW Link. 1809
BibRef

Peng, L.[Lin], Liu, J.[Jun],
Detection and analysis of large-scale WT blade surface cracks based on UAV-taken images,
IET-IPR(12), No. 11, November 2018, pp. 2059-2064.
DOI Link 1810
BibRef

Zou, Q., Zhang, Z., Li, Q., Qi, X., Wang, Q., Wang, S.,
DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection,
IP(28), No. 3, March 2019, pp. 1498-1512.
IEEE DOI 1812
crack detection, edge detection, feature extraction, image fusion, image representation, convolutional neural network BibRef

Cao, T.[Ting], Wang, W.X.[Wei-Xing], Tighe, S.[Susan], Wang, S.L.[Sheng-Lin],
Crack image detection based on fractional differential and fractal dimension,
IET-CV(13), No. 1, February 2019, pp. 79-85.
DOI Link 1902
BibRef

Goodall, T.R., Bovik, A.C.,
Detecting and Mapping Video Impairments,
IP(28), No. 6, June 2019, pp. 2680-2691.
IEEE DOI 1905
BibRef
And:
Artifact Detection Maps Learned using Shallow Convolutional Networks,
Southwest18(101-104)
IEEE DOI 1809
data compression, neural nets, object detection, probability, quantisation (signal), statistical analysis, video coding, detection. Training, Detectors, Feature extraction, Task analysis, Nonlinear distortion, Convolution, VID-MAP, Artifacts, Source Inspection BibRef

Jeelani, H., Liang, H., Acton, S.T., Weller, D.S.,
Content-Aware Enhancement of Images With Filamentous Structures,
IP(28), No. 7, July 2019, pp. 3451-3461.
IEEE DOI 1906
biomedical optical imaging, calcium, crack detection, image denoising, image enhancement, materials science computing, crack detection BibRef

Yan, Y.P.[Ya-Ping], Kaneko, S.[Shun'ichi], Asano, H.[Hirokazu],
Accumulated and aggregated shifting of intensity for defect detection on micro 3D textured surfaces,
PR(98), 2020, pp. 107057.
Elsevier DOI 1911
Defect detection, Accumulated and aggregated shifting of intensity (AASI) procedure, Illumination invariance BibRef

Liebold, F., Maas, H.G., Deutsch, J.,
Photogrammetric determination of 3D crack opening vectors from 3D displacement fields,
PandRS(164), 2020, pp. 1-10.
Elsevier DOI 2005
Material testing, Image sequence analysis, Deformation measurement, Crack analysis BibRef

Makuch, M.[Maria], Gawronek, P.[Pelagia],
3D Point Cloud Analysis for Damage Detection on Hyperboloid Cooling Tower Shells,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Marzec, M.[Mariusz], Duda, P.[Piotr], Wróbel, Z.[Zygmunt],
Analysis of microtomographic images in automatic defect localization and detection,
MVA(31), No. 5, July 2020, pp. Article35.
WWW Link. 2006
BibRef

dela Calle, F.J., García, D.[Daniel], Usamentiaga, R.[Rubén],
Generation of differential topographic images for surface inspection of long products,
RealTimeIP(17), No. 4, August 2020, pp. 967-980.
Springer DOI 2007
BibRef

Fang, F.[Fen], Li, L.Y.[Li-Yuan], Gu, Y.[Ying], Zhu, H.Y.[Hong-Yuan], Lim, J.H.[Joo-Hwee],
A novel hybrid approach for crack detection,
PR(107), 2020, pp. 107474.
Elsevier DOI 2008
Crack detection, Defect detection, Object detection, Convolutional neural network, Faster R-CNN, Bayesian fusion BibRef

Chen, F.C.[Fu-Chen], Jahanshahi, M.R.[Mohammad R.],
ARF-Crack: rotation invariant deep fully convolutional network for pixel-level crack detection,
MVA(31), No. 6, August 2020, pp. Article47.
WWW Link. 2008
BibRef

Zhang, J.B.[Jia-Bin], Su, H.[Hu], Zou, W.[Wei], Gong, X.Y.[Xin-Yi], Zhang, Z.T.[Zheng-Tao], Shen, F.[Fei],
CADN: A weakly supervised learning-based category-aware object detection network for surface defect detection,
PR(109), 2021, pp. 107571.
Elsevier DOI 2009
Weakly supervised learning, Automated surface inspection, Defect detection, Knowledge distillation BibRef

Drouyer, S.[Sébastien],
An 'All Terrain' Crack Detector Obtained by Deep Learning on Available Databases,
IPOL(10), 2020, pp. 105-123.
DOI Link 2009
Survey, Crack Detection. Code, Crack Detection. BibRef

Hu, B., Gao, B., Woo, W.L., Ruan, L., Jin, J., Yang, Y., Yu, Y.,
A Lightweight Spatial and Temporal Multi-Feature Fusion Network for Defect Detection,
IP(30), 2021, pp. 472-486.
IEEE DOI 2012
Image segmentation, Feature extraction, Semantics, Convolution, Task analysis, Principal component analysis, Data mining, defect detection BibRef

Xiang, S.[Sheng], Liang, D.[Dong], Kaneko, S.[Shun'ichi], Asano, H.[Hirokazu],
Robust defect detection in 2D images printed on 3D micro-textured surfaces by multiple paired pixel consistency in orientation codes,
IET-IPR(14), No. 14, December 2020, pp. 3373-3384.
DOI Link 2012
BibRef

Trybala, P.[Pawel], Blachowski, J.[Jan], Blazej, R.[Ryszard], Zimroz, R.[Radoslaw],
Damage Detection Based on 3D Point Cloud Data Processing from Laser Scanning of Conveyor Belt Surface,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Wu, X.H.[Xin-Hua], Liu, X.J.[Xiu-Jie],
Building crack identification and total quality management method based on deep learning,
PRL(145), 2021, pp. 225-231.
Elsevier DOI 2104
Crack detection, Image segmentation, Deep learning, Quality management, Image recognition BibRef

Zhou, Q.[Qiang], Qu, Z.[Zhong], Cao, C.[Chong],
Mixed pooling and richer attention feature fusion for crack detection,
PRL(145), 2021, pp. 96-102.
Elsevier DOI 2104
Crack detection, Mixed pooling, Spatial attention, Channel-wise attention BibRef

Qu, Z.[Zhong], Chen, W.[Wen], Wang, S.Y.[Shi-Yan], Yi, T.M.[Tu-Ming], Liu, L.[Ling],
A Crack Detection Algorithm for Concrete Pavement Based on Attention Mechanism and Multi-Features Fusion,
ITS(23), No. 8, August 2022, pp. 11710-11719.
IEEE DOI 2208
Feature extraction, Semantics, Decoding, Encoding, Intelligent transportation systems, Detection algorithms, multi-features fusion BibRef

Papadopoulos, S.[Stavros], Dimitriou, N.[Nikolaos], Drosou, A.[Anastasios], Tzovaras, D.[Dimitrios],
Modelling spatio-temporal ageing phenomena with deep Generative Adversarial Networks,
SP:IC(94), 2021, pp. 116200.
Elsevier DOI 2104
BibRef
Earlier: A2, A1, A3, A4:
A 3D-CNN Approach for the Spatio-Temporal Modeling of Surface Deterioration Phenomena,
IVMSP18(1-5)
IEEE DOI 1809
BibRef
Earlier: A1, A3, A4, Only:
Modelling of Material Ageing with Generative Adversarial Networks,
IVMSP18(1-5)
IEEE DOI 1809
Ageing simulation, Adversarial learning, Conditional GANs. Aging, Solid modeling, Training, Surface treatment, Feature extraction, Degradation. Degradation, Generators, Training, Generative adversarial networks, Aging, Task analysis BibRef

Zhang, F.[Fan], Hu, Z.Q.[Zhen-Qi], Yang, K.[Kun], Fu, Y.K.[Yao-Kun], Feng, Z.W.[Ze-Wei], Bai, M.B.[Ming-Bo],
The Surface Crack Extraction Method Based on Machine Learning of Image and Quantitative Feature Information Acquisition Method,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Rudolph, M.[Marco], Wandt, B.[Bastian], Rosenhahn, B.[Bodo],
Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows,
WACV21(1906-1915)
IEEE DOI 2106
Training, Location awareness, Fabrication, Magnetic resonance imaging, Feature extraction, Robustness, Product design BibRef

Niu, S.L.[Shuan-Long], Li, B.[Bin], Wang, X.G.[Xing-Gang], He, S.[Songping], Peng, Y.[Yaru],
Defect attention template generation cycleGAN for weakly supervised surface defect segmentation,
PR(123), 2022, pp. 108396.
Elsevier DOI 2112
Weakly supervised learning, Defect detection, Image segmentation, Generative adversarial network (GAN), Attention model BibRef

Zhu, Y.[Ying], Ding, R.[Runwei], Huang, W.[Weibo], Wei, P.[Peng], Yang, G.[Ge], Wang, Y.[Yong],
HMFCA-Net: Hierarchical multi-frequency based Channel attention net for mobile phone surface defect detection,
PRL(153), 2022, pp. 118-125.
Elsevier DOI 2201
Defect detection, HMFCA-Net, Multi-frequency channel information, Local cross-channel interaction BibRef

Wang, T.[Tao], Zhang, C.[Can], Ding, R.[Runwei], Yang, G.[Ge],
Mobile Phone Surface Defect Detection Based on Improved Faster R-CNN,
ICPR21(9371-9377)
IEEE DOI 2105
Training, Quantization (signal), Machine vision, Production, Manuals, Inspection, Feature extraction, Surface defect detection, BEGAN, RoI Align BibRef

Parrany, A.M.[Ahmad Mahdian], Mirzaei, M.[Mohsen],
A new image processing strategy for surface crack identification in building structures under non-uniform illumination,
IET-IPR(16), No. 2, 2022, pp. 407-415.
DOI Link 2201
BibRef

Hu, B.[Bing], Wang, J.H.[Jian-Hui],
A weighted multi-source domain adaptation approach for surface defect detection,
IET-IPR(16), No. 8, 2022, pp. 2210-2218.
DOI Link 2205
BibRef

Guo, J.M.[Jing-Ming], Markoni, H.[Herleeyandi], Lee, J.D.[Jiann-Der],
BARNet: Boundary Aware Refinement Network for Crack Detection,
ITS(23), No. 7, July 2022, pp. 7343-7358.
IEEE DOI 2207
Roads, Image edge detection, Feature extraction, Deep learning, Convolution, Surface cracks, Support vector machines, supervision BibRef

Angelou, N.[Nikolas], Sjöholm, M.[Mikael],
Data Reliability Enhancement for Wind-Turbine-Mounted Lidars,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Zhang, D.[Dan], Li, T.[Tieshan], Chen, C.L.P.[C.L. Philip], Wang, L.[Li],
A Xanthoceras sorbifolium crack segmentation method based on an improved level set,
IET-IPR(17), No. 5, 2023, pp. 1510-1519.
DOI Link 2304
level set, local and edge information, distance regularization, Xanthoceras sorbifolium crack segmentation BibRef

Wan, B.[Bin], Zhou, X.F.[Xiao-Fei], Zhu, B.[Bin], Xiao, M.[Mang], Sun, Y.Q.[Yao-Qi], Zheng, B.[Bolun], Zhang, J.Y.[Ji-Yong], Yan, C.G.[Cheng-Gang],
CANet: Context-aware Aggregation Network for Salient Object Detection of Surface Defects,
JVCIR(93), 2023, pp. 103820.
Elsevier DOI 2305
Defect detection, Salient object detection, Weighted convolution pyramid, Cascaded fusion structure BibRef

de León, G.[Gonzalo], Fiorentini, N.[Nicholas], Leandri, P.[Pietro], Losa, M.[Massimo],
A New Region-Based Minimal Path Selection Algorithm for Crack Detection and Ground Truth Labeling Exploiting Gabor Filters,
RS(15), No. 11, 2023, pp. 2722.
DOI Link 2306
BibRef

Zhang, Q.[Quan], Lai, J.H.[Jian-Huang], Zhu, J.Y.[Jun-Yong], Xie, X.H.[Xiao-Hua],
Wavelet-Guided Promotion-Suppression Transformer for Surface-Defect Detection,
IP(32), 2023, pp. 4517-4528.
IEEE DOI 2309
BibRef

Xiang, X.Y.[Xin-Yuan], Liu, M.Q.[Mei-Qin], Zhang, S.L.[Sen-Lin], Wei, P.[Ping], Chen, B.D.[Ba-Dong],
Multi-scale attention and dilation network for small defect detection,
PRL(172), 2023, pp. 82-88.
Elsevier DOI 2309
Small object detection, Dilated convolution blocks, Convolutional block attention module (CBAM), Perceptual field BibRef

Mao, W.S.[Wei-Sheng], Li, L.S.[Lin-Sheng], Tao, Y.F.[Yi-Fan], Zhou, W.Y.[Wen-Yi],
Surface Defect Image Classification of Lithium Battery Pole Piece Based on Deep Learning,
IEICE(E106-D), No. 9, September 2023, pp. 1546-1555.
WWW Link. 2310
BibRef

Xing, Y.[Ying], Qian, X.M.[Xiao-Meng], Guan, Y.[Yu], Yang, B.[Bin], Zhang, Y.W.[Yu-Wei],
Cross-project defect prediction based on G-LSTM model,
PRL(160), 2022, pp. 50-57.
Elsevier DOI 2208
Computational language processing, Cross-project defect prediction, Generative adversarial network BibRef

Inoue, Y.[Yuki], Nagayoshi, H.[Hiroto],
Weakly-Supervised Crack Detection,
ITS(24), No. 11, November 2023, pp. 12050-12061.
IEEE DOI 2311
BibRef

Chen, F.Q.[Fa-Quan], Deng, M.[Miaolei], Gao, H.[Hui], Yang, X.Y.[Xiao-Ya], Zhang, D.[Dexian],
NHD-YOLO: Improved YOLOv8 using optimized neck and head for product surface defect detection with data augmentation,
IET-IPR(18), No. 7, 2024, pp. 1915-1926.
DOI Link 2405
computer vision, convolutional neural nets, image recognition, object detection, quality control BibRef

Chen, H.[Hao], Qiu, J.L.[Jian-Lin], Gao, D.[Depeng], Qian, L.[Lanmei], Li, X.J.[Xiu-Jing],
Research on surface defect detection model of steel strip based on MFFA-YOLOv5,
IET-IPR(18), No. 8, 2024, pp. 2105-2113.
DOI Link 2406
convolutional neural nets, image processing, image recognition BibRef

Zhang, P.C.[Peng-Cheng], Zheng, P.X.[Pei-Xiao], Guo, X.[Xin], Chen, E.[Enqing],
Few-shot defect classification via feature aggregation based on graph neural network,
JVCIR(101), 2024, pp. 104172.
Elsevier DOI Code:
WWW Link. 2406
Few-shot learning, Graph neural networks(GNNs), Distribution learning, Surface defect classification BibRef

Li, H.[Han], Chang, X.[Xu], Hao, J.[Jinlai],
Hydraulic Fracturing Shear/Tensile/Compressive Crack Investigation Using Microseismic Data,
RS(16), No. 11, 2024, pp. 1902.
DOI Link 2406
BibRef

Zhao, L.Y.[Lang-Yue], Wu, Y.Q.[Yi-Quan], Yuan, Y.[Yubin], Tong, K.[Kang],
MACN: A cascade defect detection for complex background based on mixture attention mechanism,
IET-IPR(18), No. 9, 2024, pp. 2434-2448.
DOI Link 2407
automatic defect detection, complex background, attention network, cross-channel interaction BibRef

Hu, Z.Q.[Zi-Qiang], Chu, H.[Hao], Zhang, Y.Z.[Yun-Zhou], Shan, D.X.[De-Xing], Shen, Y.[You],
Self-supervised assisted multi-task learning network for one-shot defect segmentation with fake defect generation,
PRL(184), 2024, pp. 89-96.
Elsevier DOI Code:
WWW Link. 2408
One-shot segmentation, Self-supervised learning, Texture defect segmentation, Multi-task learning BibRef

Yang, M.H.[Ming-Hui], Liu, J.[Jing], Yang, Z.W.[Zhi-Wei], Wu, Z.Y.[Zhao-Yang],
SLSG: Industrial image anomaly detection with improved feature embeddings and one-class classification,
PR(156), 2024, pp. 110862.
Elsevier DOI 2408
Anomaly detection, One-class classification, Self-supervised learning, Graph convolutional network BibRef

Liu, B.[Binhui], Guo, T.[Tianchu], Luo, B.[Bin], Cui, Z.[Zhen], Yang, J.[Jian],
Cross-Attention Regression Flow for Defect Detection,
IP(33), 2024, pp. 5183-5193.
IEEE DOI 2410
Feature extraction, Anomaly detection, Transforms, Defect detection, Visualization, Fitting, Testing, Defect detection, autoregression BibRef

Tao, H.Q.[Hua-Qi], Liu, B.X.[Bing-Xi], Cui, J.Q.[Jin-Qiang], Zhang, H.[Hong],
A Convolutional-Transformer Network for Crack Segmentation with Boundary Awareness,
ICIP23(86-90)
IEEE DOI Code:
WWW Link. 2312
BibRef

Lei, J.R.[Jia-Rui], Hu, X.B.[Xiao-Bo], Wang, Y.[Yue], Liu, D.[Dong],
PyramidFlow: High-Resolution Defect Contrastive Localization Using Pyramid Normalizing Flow,
CVPR23(14143-14152)
IEEE DOI 2309
BibRef

Wang, Y.[Yue], Peng, J.L.[Jin-Long], Zhang, J.N.[Jiang-Ning], Yi, R.[Ran], Wang, Y.[Yabiao], Wang, C.J.[Cheng-Jie],
Multimodal Industrial Anomaly Detection via Hybrid Fusion,
CVPR23(8032-8041)
IEEE DOI 2309
BibRef

Lee, X.Y.[Xian Yeow], Vidyaratne, L.[Lasitha], Alam, M.[Mahbubul], Farahat, A.[Ahmed], Ghosh, D.[Dipanjan], Diaz, T.G.[Teresa Gonzalez], Gupta, C.[Chetan],
XDNet: A Few-Shot Meta-Learning Approach for Cross-Domain Visual Inspection,
VISION23(4375-4384)
IEEE DOI 2309
BibRef

Liu, W.Z.[Wei-Zhi], Liu, C.[Chang], Liu, Q.[Qiang], Yu, D.[Dahai],
Assigned MURA Defect Generation Based on Diffusion Model,
VISION23(4395-4402)
IEEE DOI 2309
BibRef

Jang, J.K.[Jun-Kyu], Hwang, E.[Eugene], Park, S.H.[Sung-Hyuk],
N-pad : Neighboring Pixel-based Industrial Anomaly Detection,
VISION23(4365-4374)
IEEE DOI 2309
BibRef

Xu, L.[Liang], Zou, H.[Han], Okatani, T.[Takayuki],
How Do Label Errors Affect Thin Crack Detection by DNNs,
VISION23(4414-4423)
IEEE DOI 2309
BibRef

Long, J.[Jun], Yang, Y.X.[Yu-Xi], Hua, L.[Liujie], Ou, Y.Q.[Yi-Qi],
Self-supervised Augmented Patches Segmentation for Anomaly Detection,
ACCV22(II:93-107).
Springer DOI 2307

WWW Link. BibRef

Kulkarni, S.[Shreyas], Singh, S.[Shreyas], Balakrishnan, D.[Dhananjay], Sharma, S.[Siddharth], Devunuri, S.[Saipraneeth], Korlapati, S.C.R.[Sai Chowdeswara Rao],
Crackseg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks,
CVCivil22(179-195).
Springer DOI 2304
BibRef

Zhang, X.[Xiaohu], Huang, H.F.[Hai-Feng],
LightAUNet: A Lightweight Fusing Attention Based UNet for Crack Detection,
ICIVC22(178-182)
IEEE DOI 2301
Image segmentation, Adaptation models, Convolution, Computational modeling, Transfer learning, Interference, crack segmentation BibRef

Orti, J.[Joan], Moreno-Noguer, F.[Francesc], Puig, V.[Vicenç],
Guided-Crop Image Augmentation for Small Defect Classification,
ICPR22(104-110)
IEEE DOI 2212
Industries, Training, Image segmentation, Magnetic resonance imaging, Process control, Inspection, Steel BibRef

Fang, F.[Fen], Xu, Q.L.[Qian-Li], Lim, J.H.[Joo-Hwee],
Hierarchical Defect Detection Based On Reinforcement Learning,
ICIP22(791-795)
IEEE DOI 2211
Location awareness, Deep learning, Technological innovation, Image resolution, Reinforcement learning, Object detection, High Resolution Images BibRef

Wang, B.[Bohua], Zhou, H.[Hao], Luo, W.R.[Wen-Rui], Li, C.Y.[Chen-Yang], Li, Z.B.[Zhou-Bing], Tian, Z.Q.[Zhi-Qiang],
psi-Net is an Efficient Tiny Defect Detector,
ICIP22(796-800)
IEEE DOI 2211
Adaptation models, Sensitivity, Image coding, Costs, Computational modeling, Detectors, Attention, defect defection, tiny object BibRef

Ofir, N.[Nati], Yacobi, R.[Ran], Granoviter, O.[Omer], Levant, B.[Boris], Shtalrid, O.[Ore],
Automatic Defect Segmentation by Unsupervised Anomaly Learning,
ICIP22(306-310)
IEEE DOI 2211
Training, Image segmentation, Head, Shape, Manuals, Implants, Semiconductor device manufacture, Defect Segmentation, Contrastive Learning BibRef

Tian, H.[Huang], Li, X.[Xiang], Yang, L.F.[Ling-Feng], Li, J.[Jun], Yang, J.[Jian], Du, W.D.[Wei-Dong],
PPT: Anomaly Detection Dataset of Printed Products with Templates,
ICIP22(506-510)
IEEE DOI 2211
Printing, Industries, Visualization, Inspection, Benchmark testing, Kernel, Anomaly detection, dataset, printed product, template BibRef

Chen, Z.Z.[Zhuang-Zhuang], Zhang, J.[Jin], Lai, Z.[Zhuonan], Chen, J.[Jie], Liu, Z.[Zun], Li, J.Q.[Jian-Qiang],
Geometry-Aware Guided Loss for Deep Crack Recognition,
CVPR22(4693-4702)
IEEE DOI 2210
Training, Shape, Face recognition, Benchmark testing, Noise measurement, Task analysis, Recognition: detection, Deep learning architectures and techniques BibRef

Liu, H.J.[Hua-Jun], Miao, X.Y.[Xiang-Yu], Mertz, C.[Christoph], Xu, C.Z.[Cheng-Zhong], Kong, H.[Hui],
CrackFormer: Transformer Network for Fine-Grained Crack Detection,
ICCV21(3763-3772)
IEEE DOI 2203
Semantics, Feature extraction, Transformers, Decoding, Topology, Detection and localization in 2D and 3D, grouping and shape BibRef

Rudolph, M.[Marco], Wehrbein, T.[Tom], Rosenhahn, B.[Bodo], Wandt, B.[Bastian],
Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection,
WACV22(1829-1838)
IEEE DOI 2202
Tensors, Manufacturing processes, Magnetic resonance imaging, Computational modeling, Learning and Optimization BibRef

Park, J.H.[Jin-Hyung], Chen, Y.C.[Yi-Chun], Li, Y.J.[Yu-Jhe], Kitani, K.[Kris],
Crack Detection and Refinement Via Deep Reinforcement Learning,
ICIP21(529-533)
IEEE DOI 2201
Image segmentation, Shape, Refining, Reinforcement learning, Predictive models, Prediction algorithms, Reinforcement learning, deep learning BibRef

Padalkar, M.G.[Milind G.], Beltrán-González, C.[Carlos], del Bue, A.[Alessio],
Multi-Illumination Fusion With Crack Enhancement Using Cycle-Consistent Losses,
ICIP21(2898-2902)
IEEE DOI 2201
Visualization, Tiles, Image processing, Lighting, Inspection, Generators, Multi-illumination fusion, crack detection, cycle-consistent loss BibRef

Sindel, A.[Aline], Maier, A.[Andreas], Christlein, V.[Vincent],
Craquelurenet: Matching the Crack Structure In Historical Paintings for Multi-Modal Image Registration,
ICIP21(994-998)
IEEE DOI 2201
Photography, Image registration, Visualization, Image resolution, Detectors, Fluorescence, Feature extraction, crack detection BibRef

Guan, Z.H.[Zhi-Hao], Guo, Z.D.[Zi-Dong], Lyu, J.[Jie], Yuan, Z.[Zejian],
Defect Inspection using Gravitation Loss and Soft Labels,
ICIP21(1184-1188)
IEEE DOI 2201
Image processing, Neural networks, Inspection, Noise measurement, Resins, Defect inspection, discriminative embedding features, soft labels BibRef

Lin, D.Y.[Dong-Yun], Li, Y.Q.[Yi-Qun], Prasad, S.[Shitala], Nwe, T.L.[Tin Lay], Dong, S.[Sheng], Oo, Z.M.[Zaw Min],
Cam-Guided U-Net With Adversarial Regularization for Defect Segmentation,
ICIP21(1054-1058)
IEEE DOI 2201
Training, Image segmentation, Annotations, Product design, Cams, Quality assessment, Defect Segmentation, U-Net, Adversarial Regularization BibRef

Kobayashi, H.[Hiroki], Miyoshi, R.[Ryo], Hashimoto, M.[Manabu],
Normal Image Generation-Based Defect Detection by Generative Adversarial Network with Chaotic Random Images,
ISVC21(I:353-365).
Springer DOI 2112
BibRef

Benz, C.[Christian], Rodehorst, V.[Volker],
Model-based Crack Width Estimation using Rectangle Transform,
MVA21(1-5)
DOI Link 2109
Fitting, Estimation, Transforms, Manuals, Gray-scale, Solids BibRef

Kondo, Y.[Yuki], Ukita, N.[Norimichi],
Crack Segmentation for Low-Resolution Images using Joint Learning with Super- Resolution,
MVA21(1-6)
DOI Link 2109
Image segmentation, Superresolution, Object segmentation, Kernel, Videos BibRef

Tan, D.S.[Daniel Stanley], Chen, Y.C.[Yi-Chun], Chen, T.P.C.[Trista Pei-Chun], Chen, W.C.[Wei-Chao],
TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regions,
WACV21(276-285)
IEEE DOI 2106
Training, Data collection, Noise robustness, Anomaly detection, Image reconstruction BibRef

Zhang, G.J.[Gong-Jie], Cui, K.W.[Kai-Wen], Hung, T.Y.[Tzu-Yi], Lu, S.J.[Shi-Jian],
Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection,
WACV21(2523-2533)
IEEE DOI 2106
Training, Neural networks, Inspection, Maintenance engineering, Image restoration BibRef

Padalkar, M.G.[Milind G.], Beltrán-González, C.[Carlos], Bustreo, M.[Matteo], del Bue, A.[Alessio], Murino, V.[Vittorio],
A Versatile Crack Inspection Portable System based on Classifier Ensemble and Controlled Illumination,
ICPR21(4009-4016)
IEEE DOI 2105
Training, Visualization, Tiles, Lighting, Inspection, Pattern recognition, Ceramics BibRef

Guo, T.Y.[Tian-Yu], Zhang, L.L.[Lin-Lin], Ding, R.[Runwei], Yang, G.[Ge],
EDD-Net: An Efficient Defect Detection Network,
ICPR21(8899-8905)
IEEE DOI 2105
Oils, Production, Detectors, Object detection, Tools, Mobile handsets, User experience BibRef

Božic, J.[Jakob], Tabernik, D.[Domen], Skocaj, D.[Danijel],
End-to-end training of a two-stage neural network for defect detection,
ICPR21(5619-5626)
IEEE DOI 2105
Training, Image segmentation, Uncertainty, Annotations, Neural networks, Transforms, Pattern recognition BibRef

Nava, R.[Rodrigo], Fehr, D.[Duc], Petry, F.[Frank], Tamisier, T.[Thomas],
Tire Surface Segmentation in Infrared Imaging with Convolutional Neural Networks,
IMTA20(51-62).
Springer DOI 2103
BibRef

Luan, C., Cui, R., Sun, L., Lin, Z.,
A Siamese Network Utilizing Image Structural Differences For Cross-Category Defect Detection,
ICIP20(778-782)
IEEE DOI 2011
Neural networks, Training, Testing, Indexes, Machine learning, Cats, Siamese neural network, defect detection BibRef

Lin, D., Li, Y., Prasad, S., Nwe, T.L., Dong, S., Oo, Z.M.,
CAM-UNET: Class Activation MAP Guided UNET with Feedback Refinement for Defect Segmentation,
ICIP20(2131-2135)
IEEE DOI 2011
Image segmentation, Training, Decoding, Benchmark testing, Task analysis, Cameras, Defect Segmentation, Class Activation Map, UNet BibRef

Boyadjian, Q.[Quentin], Vanderesse, N.[Nicolas], Toews, M.[Matthew], Bocher, P.[Philippe],
Detecting Defects in Materials Using Deep Convolutional Neural Networks,
ICIAR20(I:293-306).
Springer DOI 2007
BibRef

Xie, Y., Zhu, F., Fu, Y.,
Main-Secondary Network for Defect Segmentation of Textured Surface Images,
WACV20(3520-3529)
IEEE DOI 2006
Image segmentation, Feature extraction, Frequency-domain analysis, Inspection, Task analysis, Wavelet transforms BibRef

Sidorov, O., Hardeberg, J.Y.,
Craquelure as a Graph: Application of Image Processing and Graph Neural Networks to the Description of Fracture Patterns,
eHeritage19(1429-1436)
IEEE DOI 2004
art, cracks, feature extraction, graph theory, image classification, image representation, learning (artificial intelligence), Paintings classification BibRef

Ting, Y.C.[Yu-Chieh], Lin, D.T.[Daw-Tung], Chen, C.F.[Chih-Feng], Tsai, B.C.[Bor-Chen],
Automatic Optical Inspection for Millimeter Scale Probe Surface Stripping Defects Using Convolutional Neural Network,
ACIVS20(360-369).
Springer DOI 2003
BibRef

Fang, F., Li, L., Rice, M., Lim, J.,
Towards Real-Time Crack Detection Using a Deep Neural Network With a Bayesian Fusion Algorithm,
ICIP19(2976-2980)
IEEE DOI 1910
Deep neural network, objection detection, image segmentation, Bayesian fusion BibRef

Mayr, M., Hoffmann, M., Maier, A., Christlein, V.,
Weakly Supervised Segmentation of Cracks on Solar Cells Using Normalized Lp Norm,
ICIP19(1885-1889)
IEEE DOI 1910
crack detection, weakly supervised semantic segmentation, EL imaging, solar cell, normalized Lp norm BibRef

Jang, C.[Chanhee], Yun, S.[Sangyun], Hwang, H.[Hyejin], Shin, H.[Hyunmin], Kim, S.[SeongSoo], Park, Y.[Yangsub],
A Defect Inspection Method for Machine Vision Using Defect Probability Image with Deep Convolutional Neural Network,
ACCV18(I:142-154).
Springer DOI 1906
BibRef

Kobayashi, T.[Takumi],
Spiral-Net with F1-Based Optimization for Image-Based Crack Detection,
ACCV18(I:88-104).
Springer DOI 1906
BibRef

Dong, X.H.[Xing-Hui], Taylor, C.J.[Chris J.], Cootes, T.F.[Tim F.],
Small Defect Detection Using Convolutional Neural Network Features and Random Forests,
CEFR-LCV18(IV:398-412).
Springer DOI 1905
BibRef

Costa-Jover, A., Coll-Pla, S., Queral Llaberia, J., Moreno García, D., Gas Llatge, A.,
Terrestrial Laser Scanner and Fast Characterization of Superficial Lesions in Architectural Diagnosis,
3DARCH19(283-287).
DOI Link 1904
BibRef

Inoue, Y., Nagayoshi, H.,
Deployment Conscious Automatic Surface Crack Detection,
WACV19(686-694)
IEEE DOI 1904
automatic optical inspection, condition monitoring, crack detection, geotechnical structures, image segmentation, Semantics BibRef

Yan, Y., Xiang, S., Asano, H., Kaneko, S.,
Accumulated Aggregation Shifting Based on Feature Enhancement for Defect Detection on 3D Textured Low-Contrast Surfaces,
ICPR18(2965-2970)
IEEE DOI 1812
Surface treatment, Risk management, Mathematical model, Visualization, Feature extraction, Brightness BibRef

Ranzi, G.[Gianluca], Vallati, O.[Osvaldo], Cashen, I.[Ian],
A Methodology for the Inspection and Monitoring of the Roof Tiles and Concrete Components of the Sydney Opera House,
EuroMed18(I:689-699).
Springer DOI 1811
BibRef

Ma, J., Wang, Y., Shi, C., Lu, C.,
Fast Surface Defect Detection Using Improved Gabor Filters,
ICIP18(1508-1512)
IEEE DOI 1809
Surface cracks, Hysteresis, Surface treatment, Rough surfaces, Surface roughness, Standards, Surface texture, Defect Detection, Surface Inspection BibRef

Racki, D., Tomazevic, D., Skocaj, D.,
A Compact Convolutional Neural Network for Textured Surface Anomaly Detection,
WACV18(1331-1339)
IEEE DOI 1806
cellular neural nets, feature extraction, feedforward neural nets, image classification, Visualization BibRef

Kondo, N., Harada, M., Takagi, Y.,
Efficient Training for Automatic Defect Classification by Image Augmentation,
WACV18(226-233)
IEEE DOI 1806
image classification, manufacturing processes, production engineering computing, Training BibRef

Yu, N., Shen, X., Lin, Z., Mech, R., Barnes, C.,
Learning to Detect Multiple Photographic Defects,
WACV18(1387-1396)
IEEE DOI 1806
feedforward neural nets, image colour analysis, image denoising, image filtering, image restoration, Tools BibRef

Filisbino, T.A.[Tiene A.], Giraldi, G.A.[Gilson A.], Simao, L.[Lucas], Thomaz, C.E.[Carlos E.],
Combining Deep Learning and Multi-class Discriminant Analysis for Granite Tiles Classification,
WVC17(19-24)
IEEE DOI 1804
feature extraction, image classification, image texture, learning (artificial intelligence), neural nets, CNN, texture classification BibRef

Zapata, D.[Daniel], Cruz-Roa, A.[Angel], Jiménez, A.[Andrés],
Automatic Classification of Optical Defects of Mirrors from Ronchigram Images Using Bag of Visual Words and Support Vector Machines,
CIARP17(719-726).
Springer DOI 1802
BibRef

Yu, Z.Y.[Zhi-Yang], Wu, X.J.[Xiao-Jun], Gu, X.D.[Xiao-Dong],
Fully Convolutional Networks for Surface Defect Inspection in Industrial Environment,
CVS17(417-426).
Springer DOI 1711
BibRef

Bakri, A.E., Berrada, Y., Boumhidi, I.,
Bayesian regularized artificial neural network for fault detection and isolation in wind turbine,
ISCV17(1-6)
IEEE DOI 1710
Artificial neural networks, Bayes methods, Fault detection, Mathematical model, Sensors, Training, Wind turbines, Artificial neural network, Bayesian regularization, DFIG, Fault detection and isolation, Wind, Turbine BibRef

Tassine, F., Ismail, B.,
Hybrid classifier for fault detection and isolation in wind turbine based on data-driven,
ISCV17(1-8)
IEEE DOI 1710
Decision trees, Generators, Mathematical model, Neural networks, Rotors, Sensors, Wind turbines, Back-propagation Neural Networks, Bayes Statistical Algorithm, Classification, Data-Driven, Decision Tree, Fault Detection and Isolation (FDI), Learning, wind, turbine BibRef

Strisciuglio, N.[Nicola], Azzopardi, G.[George], Petkov, N.[Nicolai],
Brain-Inspired Robust Delineation Operator,
BrainDriven18(III:555-565).
Springer DOI 1905
BibRef
Earlier:
Detection of Curved Lines with B-COSFIRE Filters: A Case Study on Crack Delineation,
CAIP17(I: 108-120).
Springer DOI 1708
BibRef

Schmugge, S.J., Rice, L., Lindberg, J., Grizziy, R., Joffey, C., Shin, M.C.,
Crack Segmentation by Leveraging Multiple Frames of Varying Illumination,
WACV17(1045-1053)
IEEE DOI 1609
Image segmentation, Inspection, Lighting, Power generation, Videos, Visualization, Welding BibRef

Villalon-Hernandez, M.T.[Miyuki-Teri], Almanza-Ojeda, D.L.[Dora-Luz], Ibarra-Manzano, M.A.[Mario-Alberto],
Color-Texture Image Analysis for Automatic Failure Detection in Tiles,
MCPR17(159-168).
Springer DOI 1706
BibRef

Veitch-Michaelis, J., Tao, Y., Walton, D., Muller, J.P., Crutchley, B., Storey, J., Paterson, C., Chown, A.,
Crack Detection in 'As-Cast' Steel Using Laser Triangulation and Machine Learning,
CRV16(342-349)
IEEE DOI 1612
3D reconstruction BibRef

Kubatur, S.S., Comer, M.L.,
Rare event simulation for Markov random fields with application to grain growth in crystals,
ICIP16(3748-3752)
IEEE DOI 1610
Computational modeling BibRef

Chen, P.H., Ho, S.S.,
Is overfeat useful for image-based surface defect classification tasks?,
ICIP16(749-753)
IEEE DOI 1610
Feature extraction BibRef

Loyola-González, O.[Octavio], Martínez-Trinidad, J.F.[José F.], Carrasco-Ochoa, J.A.[Jesús A.], Hernández-Tamayo, D.[Dayton], García-Borroto, M.[Milton],
Detecting Pneumatic Failures on Temporary Immersion Bioreactors,
MCPR16(293-302).
Springer DOI 1608
BibRef

López-Leyva, R.[Rafael], Rojas-Domínguez, A.[Alfonso], Flores-Mendozaa, J.P.[Juan Pablo], Casillas-Araiza, M.Á.[Miguel Ángel], Santiago-Montero, R.[Raúl],
Comparing Threshold-Selection Methods for Image Segmentation: Application to Defect Detection in Automated Visual Inspection Systems,
MCPR16(33-43).
Springer DOI 1608
BibRef

Schmugge, S.J., Rice, L., Nguyen, N.R., Lindberg, J., Grizzi, R., Joffe, C., Shin, M.C.,
Detection of cracks in nuclear power plant using spatial-temporal grouping of local patches,
WACV16(1-7)
IEEE DOI 1606
Imaging BibRef

Chmielewski, L.J.[Leszek J.], Orlowski, A.[Arkadiusz], Smietanska, K.[Katarzyna], Górski, J.[Jaroslaw], Krajewski, K.[Krzysztof], Janowicz, M.[Maciej], Wilkowski, J.[Jacek], Kietlinska, K.[Krystyna],
Detection of Surface Defects of Type 'orange skin' in Furniture Elements with Conventional Image Processing Methods,
GPID15(26-37).
Springer DOI 1603
BibRef

Williams, C.D.[Christopher D.], Paul, M.[Manoranjan], Debnath, T.[Tanmoy],
Enhancing Automated Defect Detection in Collagen Based Manufacturing by Employing a Smart Machine Vision Technique,
RV15(155-166).
Springer DOI 1603
BibRef

Stent, S.[Simon], Gherardi, R.[Riccardo], Stenger, B.[Björn], Cipolla, R.[Roberto],
Detecting Change for Multi-View, Long-Term Surface Inspection,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Cheng, J.R.[Jie-Rong], Xiong, W.[Wei], Wang, Y.[Yue], Chia, S.C.[Shue Ching], Chen, W.Y.[Wen-Yu], Du, J.[Jia], Gu, Y.[Ying], Kow, V.T.S.[Victor Ter Shen],
CHORD: Cascaded and a contrario method for hole crack detection,
ICIP15(3300-3304)
IEEE DOI 1512
BibRef

Lagache, T.[Thibault], Marcou, Q.[Quentin], Bardonnet, A.[Antoine], Rotureau, B.[Brice], Bastin, P.[Philippe], Olivo-Marin, J.C.[Jean-Christophe],
Using steerable wavelets and minimal paths to reconstruct automatically filaments in fluorescence imaging,
ICIP15(706-709)
IEEE DOI 1512
Filament detection BibRef

Funahashi, T., Taki, K., Koshimizu, H., Kaneko, A.,
Fast and robust visual inspection system for tire surface thin defect,
FCV15(1-6)
IEEE DOI 1506
inspection BibRef

Drogoul, A.[Audric], Aubert, G.[Gilles], Auroux, D.[Didier],
Topological gradient for a fourth order PDE and application to the detection of fine structures in 2D and 3D images,
ICIP14(1703-1707)
IEEE DOI 1502
e.g. filaments. BibRef

Tsai, Y.C.J.[Yi-Chang James], Jiang, C.L.[Cheng-Long], Wang, Z.H.[Zhao-Hua],
Implementation of automatic crack evaluation using Crack Fundamental Element,
ICIP14(773-777)
IEEE DOI 1502
Accuracy BibRef

von Enzberg, S.[Sebastian], Al-Hamadi, A.[Ayoub],
A Defect Recognition System for Automated Inspection of Non-rigid Surfaces,
ICPR14(1812-1816)
IEEE DOI 1412
Inspection BibRef

Ma, L.[Ling], Liu, W.[Wei], Liu, Y.M.[Yu-Min], Jiang, H.Q.[Hui-Qin],
An Automatic Detection Algorithm for Surface Defects in TFT-LCD,
ACPR13(847-851)
IEEE DOI 1408
cost reduction BibRef

Landstrom, A., Thurley, M.J., Jonsson, H.,
Sub-Millimeter Crack Detection in Casted Steel Using Color Photometric Stereo,
DICTA13(1-7)
IEEE DOI 1412
cracks BibRef

Delis, S.[Sotirios], Nikolaidis, N.[Nikos], Pitas, I.[Ioannis],
Automatic 3D defects identification in stereoscopic videos,
ICIP13(2227-2231)
IEEE DOI 1412
3D quality; disparity; stereo video BibRef

Briceño, C.[Carlos], Rivera-Rovelo, J.[Jorge], Acuña, N.[Narciso],
Crack's Detection, Measuring and Counting for Resistance's Tests Using Images,
CIARP13(II:142-149).
Springer DOI 1311
BibRef

Hahn, A.[Andreas], Ziebarth, M.[Mathias], Heizmann, M.[Michael], Rieder, A.[Andreas],
Defect Classification on Specular Surfaces Using Wavelets,
SSVM13(501-512).
Springer DOI 1305
BibRef

Choi, J.[Jiwon], Kim, C.[Changick],
Unsupervised detection of surface defects: A two-step approach,
ICIP12(1037-1040).
IEEE DOI 1302
BibRef

von Enzberg, S.[Sebastian], Michaelis, B.[Bernd],
Surface Quality Inspection of Deformable Parts with Variable B-spline Surfaces,
DAGM12(175-184).
Springer DOI 1209
BibRef

Shih, Y.C.[Yi Chang], Davis, A.[Abe], Hasinoff, S.W.[Samuel W.], Durand, F.[Fredo], Freeman, W.T.[William T.],
Laser speckle photography for surface tampering detection,
CVPR12(33-40).
IEEE DOI 1208
BibRef

Liu, L.H.[Ling-Hui], Zeng, L.[Li], Bi, B.[Bi],
A Unified Method Based on Wavelet Transform and C-V Model for Crack Segmentation of 3D Industrial CT Images,
ICIG11(12-16).
IEEE DOI 1109
BibRef

Hu, H.[Han], Gu, Q.Q.[Quan-Quan], Zhou, J.[Jie],
HTF: a novel feature for general crack detection,
ICIP10(1633-1636).
IEEE DOI 1009
BibRef

Oliveira, H.[Henrique], Correia, P.L.[Paulo Lobato],
CrackIT: An image processing toolbox for crack detection and characterization,
ICIP14(798-802)
IEEE DOI 1502
Classification algorithms BibRef

Hampel, U.,
Crack Detection In Load Tests For Civil Engineering Material Testing By Digital Closed Range Photogrammetry: Algorithms And Applications,
CloseRange10(xx-yy).
PDF File. 1006
BibRef

Skarlatos, D., Bakolias, C.,
Industrial Inspection And Checking Of Marble Tiles,
CloseRange10(xx-yy).
PDF File. 1006
BibRef

Aiger, D.[Dror], Talbot, H.[Hugues],
The phase only transform for unsupervised surface defect detection,
CVPR10(295-302).
IEEE DOI 1006
BibRef

Cheng, J.Z.[Jian-Zheng], Li, D.J.[De-Jun], Wei, Z.G.[Zhi-Gang], Chu, M.J.[Mei-Juan], Zhang, D.J.[De-Jun],
Application of Fuzzy Pattern Recognition in Ultrasonic Transverse Wave Detection of Wheel Flaws,
CISP09(1-5).
IEEE DOI 0910
BibRef

Nakazawa, M.[Mitsuru], Aoki, Y.[Yoshimitsu], Kobayashi, M.[Masakazu], Toda, H.[Hiroyuki],
3D image analysis for evaluating internal deformation/fracture characteristics of materials,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Tirronen, V.[Ville], Neri, F.[Ferrante], Karkkainen, T.[Tommi], Majava, K.[Kirsi], Rossi, T.[Tuomo],
A Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production,
EvoIASP07(320-329).
Springer DOI 0704
BibRef

d'Orazio, T., Leo, M., Guaragnella, C., Distante, A.,
Analysis of Image Sequences for Defect Detection in Composite Materials,
ACIVS07(855-864).
Springer DOI 0708
BibRef

Yue, K.[Kui], Huber, D.F.[Daniel F.], Akinci, B.[Burcu], Krishnamurti, R.[Ramesh],
The ASDMCon Project: The Challenge of Detecting Defects on Construction Sites,
3DPVT06(1048-1055).
IEEE DOI 0606
BibRef

Chowdhury, A.S., Bhattacharya, A., Bhandarkar, S.M., Datta, G.S., Yu, J.C., Figueroa, R.,
Hairline Fracture Detection using MRF and Gibbs Sampling,
WACV07(56-56).
IEEE DOI 0702
BibRef

Urano, T., Kaneko, S., Tanaka, T.,
Robust registration of defect set by local consistency of point data,
IEVM06(xx-yy).
PDF File. 0609
BibRef

Frau, D.C.[David Cuesta], Hernández-Fenollosa, M.Á.[María Ángeles], Tormos, P.M.[Pau Micó], Linares-Pellicer, J.[Jordi],
Segmentation of Nanocolumnar Crystals from Microscopic Images,
ICIAR05(55-62).
Springer DOI 0509
BibRef

Limas Serafim, A.F.,
Segmentation of natural images based on multiresolution pyramids linking of the parameters of an autoregressive rotation invariant model. Application to leather defects detection,
ICPR92(III:41-44).
IEEE DOI 9208
BibRef

Vieira, S.M.[Susana M.], Sousa, J.M.C.[João M. C.], Pinto, J.R.C.[João R. Caldas],
Ant Based Fuzzy Modeling Applied to Marble Classification,
ICIAR06(II: 90-101).
Springer DOI 0610

See also Intelligent Real-Time Fabric Defect Detection. BibRef

Sousa, J.M.C.[João M.C.], Pinto, J.R.C.[João R. Caldas],
Comparison of Intelligent Classification Techniques Applied to Marble Classification,
ICIAR04(II: 802-809).
Springer DOI 0409
BibRef

Amano, T.[Toshiyuki],
Correlation Based Image Defect Detection,
ICPR06(I: 163-166).
IEEE DOI 0609
BibRef

Bruno, R.[Roberto], Cuoghi, L.[Lorenza], Laurenge, P.[Pascal],
Quantitative Identification of Marbles Aesthetical Features,
IbPRIA05(II:674).
Springer DOI 0509
BibRef

Viana, R.[Roberto], Rodrigues, R.B.[Ricardo B.], Alvarez, M.A.[Marco A.], Pistori, H.[Hemerson],
SVM with Stochastic Parameter Selection for Bovine Leather Defect Classification,
PSIVT07(600-612).
Springer DOI 0712
BibRef

Ai, J.Y.[Jiao-Yan], Di, L.[Liu], Zhu, X.F.[Xue-Feng],
Combination of wavelet analysis and color applied to automatic color grading of ceramic tiles,
ICPR04(III: 235-238).
IEEE DOI 0409
BibRef

Jia, H.B.[Hong-Bin], Murphey, Y.L.[Yi Lu], Shi, J.J.[Jian-Jun], Chang, T.S.[Tzyy-Shuh],
An intelligent real-time vision system for surface defect detection,
ICPR04(III: 239-242).
IEEE DOI 0409
BibRef

Eisele, H., Hamprecht, F.A.,
A New Approach for Defect Detection in X-ray CT Images,
DAGM02(345 ff.).
Springer DOI 0303
BibRef

Toth, D., Condurache, A.P.[Alexandru Paul], Aach, T.[Til],
A two-stage-classifier for defect classification in optical media inspection,
ICPR02(IV: 373-376).
IEEE DOI 0211
BibRef

Gupta, P., Doermann, D., DeMenthon, D.F.,
Beam search for feature selection in automatic SVM defect classification,
ICPR02(II: 212-215).
IEEE DOI 0211
BibRef

Maalmi, K., El-Ouaazizi, A., Benslimane, R., Lew, L.F.C., Voon, Y.[Yan], Diou, A., Gorria, P.,
Crack defect detection and localization using genetic-based inverse voting Hough transform,
ICPR02(III: 257-260).
IEEE DOI 0211
BibRef

Martínez-Cabeza-de-Vaca-Alajarín, J.[Juan], Tomás-Balibrea, L.M.[Luis-Manuel],
Automatic Classification System of Marble Slabs in Production Line According to Texture and Color Using Artificial Neural Networks,
CAIP99(167-174).
Springer DOI 9909
BibRef

Chetverikov, D.[Dmitry], Khenokh, Y.[Yuri],
Matching for Shape Defect Detection,
CAIP99(367-374).
Springer DOI 9909
BibRef

Kobayashi, H.H., Hara, Y., Doi, H., Takai, K., Sumiya, A.,
Hybrid Defect Detection Method Based on Shape Measurement and Feature Extraction for Complex Patterns,
MVA98(xx-yy). BibRef 9800

Pakkanen, J.[Jussi], Ilvesmäki, A.[Antti], Iivarinen, J.[Jukka],
Defect Image Classification and Retrieval with MPEG-7 Descriptors,
SCIA03(349-355).
Springer DOI 0310
BibRef

Visa, A.[Ari], Iivarinen, J.[Jukka],
An Adaptive Texture and Shape Based Defect Classification,
ICPR98(Vol I: 117-122).
IEEE DOI 9808
BibRef

Iivarinen, J.[Jukka], Rauhamaa, J.[Juhani], and Visa, A.[Ari],
An Adaptive Two-Stage Approach to Classification of Surface Defects,
SCIA97(xx-yy)
HTML Version. 9705
BibRef

Anzalone, A., Machí, A.,
Visual detection of defects in moulded plastic drippers,
CAIP95(802-807).
Springer DOI 9509
BibRef

Bruzzone, L., Roli, F., Serpico, S.B.,
Crack detection by a measure of texture anisotropy,
CIAP95(743-747).
Springer DOI 9509
BibRef

Azencott, R., Yao, J.,
Automated detection of cowhide defects using Markov random field techniques,
ICPR94(A:791-793).
IEEE DOI 9410
BibRef

Sokolov, S.M., Treskunov, A.S.,
Automatic vision system for final test of liquid crystal display,
CRA92(1578-1582).
WWW Link. BibRef 9200

Silven, O., Westman, T., Huotari, S., Hakalahti, H.,
A Defect Analysis Method for Visual Inspection,
ICPR86(868-870). BibRef 8600

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Inspection -- Glass, Panes, Panels, Bottles .