[1] Alaluf A, Birnbaum D. Inspection of PCBs by laser: induced fluorescence[J]. Circuit World, 28, 21-28(2002). http://www.emeraldinsight.com/doi/full/10.1108/03056120210407702
[2] Mar N S S, Fookes C. Design and development of automatic visual inspection system for PCB manufacturing[J]. Robotics and Computer-Integrated Manufacturing, 27, 949-962(2011). http://www.sciencedirect.com/science/article/pii/S0736584511000457
[3] Yang S W, Lin C S, Lin S K et al. Automatic defect recognition of TFT array process using gray level co-occurrence matrix[J]. Optik-International Journal for Light and Electron Optics, 125, 2671-2676(2014). http://www.sciencedirect.com/science/article/pii/S0030402614001156
[4] Neogi N, Mohanta D K, Dutta P K. Review of vision-based steel surface inspection systems[J]. EURASIP Journal on Image and Video Processing, 2014, 50(2014). http://link.springer.com/10.1186/1687-5281-2014-50
[5] Min Y Z, Xiao B Y, Dang J W et al. Real time detection system for rail surface defects based on machine vision[J]. EURASIP Journal on Image and Video Processing, 2018, 3(2018). http://link.springer.com/10.1186/s13640-017-0241-y
[6] Shi Y Q, Lu R S, Zhang T D. Defect inspection system design based on the automated optical inspection technique for LCD backlight modules[J]. Chinese Journal of Sensors and Actuators, 28, 768-773(2015).
[7] Janoczki M, Becker A, Jakab L et al. Automatic optical inspection of soldering, materials science[M]. Yitzhak Mastai: IntechOpen(2013).
[8] marking systems, methods: US09/941416[P]. -03-06. Smith A. Automated optical inspection(2003).
[9] Lu R S. State of the art of automated optical inspection[J]. Infrared and Laser Engineering, 37, 120-123(2008).
[10] Lu R S, Forrest A K. 3D surface topography from the specular lobe of scattered light[J]. Optics and Lasers in Engineering, 45, 1018-1027(2007). http://www.sciencedirect.com/science/article/pii/S0143816607000905
[11] Lu R S, Shi Y Q, Li Q et al. AOI techniques for surface defect inspection[J]. Applied Mechanics and Materials, 36, 297-302(2010). http://www.scientific.net/AMM.36.297
[12] Dong J T. Study on the 3D micro/nano measurement system and techniques based on Linnik polarization-sensitive white light interferometry[D]. Hefei: Hefei University of Technology(2012).
[13] Li Q. Surface defects AOI inspection technology and system architecture[D]. Hefei: Hefei University of Technology(2012).
[14] Tian G Y, Lu R S, Gledhill D. Surface measurement using active vision and light scattering[J]. Optics and Lasers in Engineering, 45, 131-139(2007). http://www.sciencedirect.com/science/article/pii/S0143816606000844
[15] Smith B. Geometrical shadowing of a random rough surface[J]. IEEE Transactions on Antennas and Propagation, 15, 668-671(1967). http://doi.ieeecomputersociety.org/resolve?ref_id=doi:10.1109/TAP.1967.1138991&rfr_id=trans/tp/1991/02/ttp1991020133.htm
[16] Tsang L, Kong J A, Ding K H. Scattering of electromagnetic waves, theories and applications[M]. New York: John Wisley & Sons Inc.(2000).
[17] Collin R E. Electromagnetic scattering from perfectly conducting rough surfaces (a new full wave method)[J]. IEEE Transactions on Antennas and Propagation, 40, 1466-1477(1992).
[18] Nayar S K, Ikeuchi K, Kanade T. Surface reflection: physical and geometrical perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 611-634(1991). http://doi.ieeecomputersociety.org/10.1109/34.85654
[19] Beckmann P, Spizzochino A. The scattering of electro-magnetic waves from rough surfaces[M]. New York: Pergamon(1963).
[20] Liu Y, Yu F H. Automatic inspection system of surface defects on optical IR-CUT filter based on machine vision[J]. Optics and Lasers in Engineering, 55, 243-257(2014). http://www.sciencedirect.com/science/article/pii/S0143816613003485
[21] Martin D. A practical guide to machine vision lighting[J]. Retrieved, 11, 2013(2007). http://www.researchgate.net/publication/288257230_A_practical_guide_to_machine_vision_lighting
[22] Muehlemann M. Standardizing defect detection for the surface inspection of large web steel[M]. New York: Illumination Technologies Inc, 1-9(2000).
[24] Vriesenga M, Healey G, Peleg K et al. Controlling illumination color to enhance object discriminability. [C]∥Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 15-18, 1992, Champaign, IL, USA. New York: IEEE, 710-712(1992).
[25] Garcia-Lamont F, Cervantes J, López A et al. Segmentation of images by color features: a survey[J]. Neurocomputing, 292, 1-27(2018). http://www.sciencedirect.com/science/article/pii/S0925231218302364
[26] Healey G E, Kondepudy R. Radiometric CCD camera calibration and noise estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 267-276(1994). http://doi.ieeecomputersociety.org/10.1109/34.276126
[27] Chang Y C, Reid J F. RGB calibration for color image analysis in machine vision[J]. IEEE Transactions on Image Processing A, 5, 1414-1422(1996). http://europepmc.org/abstract/MED/18290059
[31] Wu F P, Zhang X M. An inspection and classification method for chip solder joints using color grads and Boolean rules[J]. Robotics and Computer-Integrated Manufacturing, 30, 517-526(2014).
[32] Kim T H, Cho T H, Moon Y S et al. Visual inspection system for the classification of solder joints[J]. Pattern Recognition, 32, 565-575(1999). http://www.sciencedirect.com/science/article/pii/S0031320398001034
[33] Hao W, Xianmin Z, Yongcong K et al. Solder joint inspection based on neural network combined with genetic algorithm[J]. Optik-International Journal for Light and Electron Optics, 124, 4110-4116(2013). http://www.sciencedirect.com/science/article/pii/S0030402613000478
[34] Infrared vision[EB/OL]∥Wikipedia, the free encyclopedia. -03-19)[2018-05-16]. https:∥en.wikipedia.org/w/index.php?title=Infrared_vision&oldid=831150773.(2018).
[37] Flanagan D. Infrared machine vision: a new contender[J]. Sensors-The Journal of Applied Sensing Technology, 15, 29-33(1998). http://www.researchgate.net/publication/293440714_Infrared_machine_vision_-_a_new_contender
[38] Ibarra-Castanedo C, Sfarra S, Genest M et al. Infrared vision: visual inspection beyond the visible spectrum[M]. ∥Liu Z, Ukida H, Ramuhalli P, et al. Integrated imaging and vision techniques for industrial inspection. London: Springer, 41-58(2015).
[39] Rogalski A. Recent progress in infrared detector technologies[J]. Infrared Physics & Technology, 54, 136-154(2011). http://www.sciencedirect.com/science/article/pii/S1350449510001040
[40] Razeghi M, Nguyen B M. Advances in mid-infrared detection and imaging: a key issues review[J]. Reports on Progress in Physics, 77, 082401(2014). http://www.ncbi.nlm.nih.gov/pubmed/25093341
[41] Rogalski A, Martyniuk P, Kopytko M. Challenges of small-pixel infrared detectors: a review[J]. Reports on Progress in Physics, 79, 046501(2016). http://europepmc.org/abstract/MED/27007242
[42] Malchow D. Shortwave IR imaging in machine vision: principles and practice-before you specify your next machine vision system, check out what SWIR cameras can do for your manufacturing process[J]. Sensors-the Journal of Applied Sensing Technology, 21, 19-25(2004). http://dialnet.unirioja.es/servlet/articulo?codigo=1020903
[43] Kabouri A, Khabbazi A, Youlal H. Applied multiresolution analysis to infrared images for defects detection in materials[J]. NDT & E International, 92, 38-49(2017). http://www.sciencedirect.com/science/article/pii/S0963869517300725
[44] Kim G. Micro defect detection in solar cell wafer based on hybrid illumination and near-infrared optics. [C]∥2013 9th Asian Control Conference (ASCC), June 23-26, 2013, Istanbul, Turkey. New York: IEEE, 1-5(2013).
[45] Hamdi A A, Fouad M M, Sayed M S. Patterned fabric defect detection system using near infrared imaging. [C]∥2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), December 05-07, 2017, Cairo, Egypt. New York: IEEE, 111-117(2017).
[46] Zhang X W, Ding Y Q, Yan P. Vision inspection of metal surface defects based on infrared imaging[J]. Acta Optica Sinica, 31, 0312004(2011).
[47] Aldave I J, Bosom P V, Gonzalez L V et al. Review of thermal imaging systems in composite defect detection[J]. Infrared Physics & Technology, 61, 167-175(2013). http://www.sciencedirect.com/science/article/pii/S1350449513001448
[48] Chen D, Zhang X, Zhang G et al. Infrared thermography and its applications in aircraft non-destructive testing. [C]∥2016 International Conference on Identification, Information and Knowledge in the Internet of Things(IIKI), October 20-21, 2016, Beijing, China. New York: IEEE, 374-379(2016).
[49] Tang C Q, Tian G Y, Chen X T et al. Infrared and visible images registration with adaptable local-global feature integration for rail inspection[J]. Infrared Physics & Technology, 87, 31-39(2017).
[50] Liu J, Tian G Y, Gao B et al. Investigation of thermal imaging sampling frequency for eddy current pulsed thermography[J]. NDT & E International, 62, 85-92(2014). http://www.sciencedirect.com/science/article/pii/S0963869513001540
[51] He Y Z, Du B L, Huang S D. Non-contact electromagnetic induction excited infrared thermography for photovoltaic cells and modules inspection[J]. IEEE Transactions on Industrial Informatics, 1(2018).
[52] Vavilov V P, Burleigh D D. Review of pulsed thermal NDT: Physical principles, theory and data processing[J]. NDT & E International, 73, 28-52(2015). http://www.sciencedirect.com/science/article/pii/S0963869515000389
[53] Andonova A, Angelov G, Chernev P. Diagnostics of packaged ICs by infrared thermography. [C]∥Proceedings of the 2014 37th International Spring Seminar on Electronics Technology, May 07-11, 2014, Dresden, Germany. New York: IEEE, 261-266(2014).
[54] Vavilov V P. Modeling thermal NDT problems[J]. International Journal of Heat and Mass Transfer, 72, 75-86(2014).
[55] Yang R Z, He Y Z. Corrigendum for “Optically and non-optically excited thermography for composites: A review”. [Infrared Phys. Technol. 75 (2016) 26-50][J]. Infrared Physics & Technology, 76, 259-260(2016).
[57] Katila P. UV. -03-23)[2018-05-12]. http:∥www.ledsmagazine.com/articles/print/volume-12/issue-3/features/thermal/uv-and-industrial-led-applications-require-efficient-thermal-substrates.html.(2015).
[61] Kuo S H, Chen C F. Design of a collimated UV-LED exposure unit based on light spread function method[J]. Applied Optics, 56, 5542-5549(2017). http://europepmc.org/abstract/MED/29047515
[63] Daloglu M U, Ray A, Gorocs Z et al. On-chip microscopy and nano-particle detection using ultraviolet light. [C]∥2017 Conference on Lasers and Electro-Optics (CLEO), May 14-19, 2017, San Jose, CA, USA. New York: IEEE, 1-2(2017).
[64] During A, Fossati C, Commandré M. Multiwavelength imaging of defects in ultraviolet optical materials[J]. Applied Optics, 41, 3118-3126(2002). http://www.opticsinfobase.org/ao/abstract.cfm?uri=ao-41-16-3118
[65] Chen J, Tretiak O J. Fluorescence imaging for machine vision[J]. Applied Optics, 31, 1871-1877(1992). http://www.ncbi.nlm.nih.gov/pubmed/20720830
[67] Rady A M, Guyer D E. Rapid and/or nondestructive quality evaluation methods for potatoes: A review[J]. Computers and Electronics in Agriculture, 117, 31-48(2015). http://www.sciencedirect.com/science/article/pii/S0168169915001970
[68] Hecht E. Optics[M]. 4th ed. New Jersey: Addison Wesley(2002).
[69] Liang S K[M]. Physical optics(1986).
[71] Yu D Y, Tan H Y[M]. Engineering optics(1999).
[72] Successful Light Polarization Techniques[EB/OL]. -03-11)[2018-05-12]. https:∥www.edmundoptics.com/resources/application-notes/illumination/successful-light-polarization-techniques/.(2014).
[73] Tianjin University[M]. Teaching and Research Office of Material Mechanics, Photoelastic Group. Photoelastic principle and testing technology(1982).
[75] Wei X H, Gao B, Li Q et al. Study of the stress birefringence measurement of optical glass[J]. Acta Optica Sinica, 35, s212002(2015).
[76] Yoshizawa T[M]. Handbook of optical metrology, principles and applications(2009).
[79] Lekholm V, Rämme G, Thornell G. Seeing the invisible with schlieren imaging[J]. Physics Education, 46, 294-297(2011). http://eric.ed.gov/?id=EJ941346
[80] Gross H, Hofmann M, Jedamzik R et al. Measurement and simulation of striae in optical glass[J]. Proceedings of SPIE, 7389, 73891C(2009). http://proceedings.spiedigitallibrary.org/mobile/proceeding.aspx?articleid=784264
[81] Raffel M. Background-oriented schlieren (BOS) techniques[J]. Experiments in Fluids, 56, 56-60(2015). http://link.springer.com/article/10.1007/s00348-015-1927-5
[82] Settles G S. Smartphone schlieren and shadowgraph imaging[J]. Optics and Lasers in Engineering, 104, 9-21(2018). http://www.sciencedirect.com/science/article/pii/S0143816617303858
[83] Hargather M J, Settles G S. A comparison of three quantitative schlieren techniques[J]. Optics and Lasers in Engineering, 50, 8-17(2012). http://www.sciencedirect.com/science/article/pii/S014381661100159X?_rdoc=3&_fmt=high&_origin=browse&_srch=hubEid(1-s2.0-S0143816611X00106)&_docanchor=&_ct=12&_refLink=Y&_zone=rslt_list_item&md5=07934c934c375c4b404492b9d33d213a
[84] Meyer J, Gruna R, Längle T et al[J]. Simulation of an inverse schlieren image acquisition system for inspecting transparent objects Electronic Imaging, 2016, 1-9.
[85] Batchelor B G. Machine vision handbook[M]. London, UK: Springer(2012).
[86] Beyerer J, Leon F P, Frese C. Machine vision, automated vision inspection: theory, practice and application[M]. London, UK: Springer(2016).
[87] Panigrahi P K, Muralidhar K. Schlieren and shadowgraph methods in heat and mass transfer[M]. Berlin: Springer(2012).
[88] Settles G S. Schlieren and shadowgraph techniques[M]. Berlin: Springer(2001).
[89] Mazumdar A. Principles and techniques of schlieren imaging systems[J]. Columbia University Computer Science Technical Reports, 14(2013). http://www.mendeley.com/research/principles-techniques-schlieren-imaging/
[90] Osten W. Digital image processing for optical metrology[M]. ∥William Sharpe, Springer handbook of experimental solid mechanics. Boston: Springer, 481-564(2008).
[91] Schnars U, Jueptner W. Digital holography, digital hologram recording, numerical reconstruction and related techniques[M]. Berlin: Springer(2005).
[92] Kreis T. Application of digital holography for nondestructive testing and metrology: a review[J]. IEEE Transactions on Industrial Informatics, 12, 240-247(2016). http://ieeexplore.ieee.org/document/7279125/
[93] Acharya I, Upadhyay D. Comparative study of digital holography reconstruction methods[J]. Procedia Computer Science, 58, 649-658(2015). http://www.sciencedirect.com/science/article/pii/S187705091502195X
[94] Lu J P, Tahara T, Hayasaki Y et al. Incoherent digital holography: a review[J]. Applied Sciences, 8, 143(2018). http://www.researchgate.net/publication/322639299_Incoherent_Digital_Holography_A_Review
[95] Pedrini G, Tiziani H J. Quantitative evaluation of two-dimensional dynamic deformations using digital holography[J]. Optics & Laser Technology, 29, 249-256(1997). http://www.sciencedirect.com/science/article/pii/S0030399297000042
[96] Osten W, Faridian A, Gao P et al. Recent advances in digital holography [invited][J]. Applied Optics, 53, G44-G63(2014). http://www.opticsinfobase.org/abstract.cfm?URI=ao-53-27-G44
[97] Kim M K. Digital holographic microscopy, principles, techniques, and applications[M]. New York: Springer(2011).
[98] Inspection in Industry. Braunschweig, Germany[2018-05-12]. 2011 http:∥www.imeko.org/publications/tc14-2011/IMEKO-TC14-2011-10.pdf.(2011).
[99] Yamaguchi I. Holography, speckle, and computers[J]. Optics and Lasers in Engineering, 39, 411-429(2003).
[100] Jacquot P. Speckle interferometry: A review of the principal methods in use for experimental mechanics applications[J]. Strain, 44, 57-69(2008).
[101] Yang L X, Siebert T. Digital speckle interferometry in engineering[J]. New Directions in Holography and Speckle, 405-440(2008).
[102] Groves R M, Pradarutti B, Kouloumpi E et al. 2D and 3D non-destructive evaluation of a wooden panel painting using shearography and terahertz imaging[J]. NDT & E International, 42, 543-549(2009).
[103] Rao M V, Samuel R, Ananthan A. Applications of electronic speckle interferometry (ESI) techniques for spacecraft structural components[J]. Optics and Lasers in Engineering, 40, 563-571(2003).
[104] Raman R K S, Bayles R. Detection of decohesion/failure of paint/coating using electronic speckle pattern interferometry[J]. Engineering Failure Analysis, 13, 1051-1056(2006). http://www.sciencedirect.com/science/article/pii/S1350630705001718
[105] Hung Y Y. Shearography: A novel and practical approach for nondestructive inspection[J]. Journal of Nondestructive Evaluation, 8, 55-67(1989). http://link.springer.com/article/10.1007/BF00565631
[106] Feng J Y, Wang Y H, Wang X et al. Design of digital shearography with wide angle of view based on 4f system[J]. Journal of Applied Optics, 36, 188-193(2015).
[107] Xie X, Yang L X, Xu N et al. Michelson interferometer based spatial phase shift shearography[J]. Applied Optics, 52, 4063-4071(2013). http://www.opticsinfobase.org/abstract.cfm?URI=ao-52-17-4063
[108] Liu P, Wang Y H, Feng J Y et al. Phase detection technology in spatial carrier based on deflection angle[J]. Opto-Electronic Engineering, 42, 39-43(2015).
[109] Steinchen W, Yang L X[M]. Digital shearography-theory and application of digital speckle pattern interferometry(2003).
[110] Wang Y H, Thomsa D, Zhang P et al. Whole field strain measurement on complex surfaces by digital speckle pattern interferometry[J]. Materials Evaluation, 66, 507-512(2008). http://pubmedcentralcanada.ca/pmcc/articles/PMC3122977/
[111] Wang Y H, Lü Y B, Gao X Y et al. Research progress in shearography and its applications[J]. Chinese Journal of Optics, 10, 300-309(2017).
[112] Hanzaei S H, Afshar A, Barazandeh F. Automatic detection and classification of the ceramic tiles’ surface defects[J]. Pattern Recognition, 66, 174-189(2017). http://www.researchgate.net/publication/310837233_Automatic_Detection_and_Classification_of_the_Ceramic_Tiles'_Surface_Defects
[113] General Administration of Quality Supervision, Inspection, Quarantine of the People's Republic of China, . Geometrical Product Specification(GPS)-Surface imperfections-Terms,(2002).
[114] Whitehouse D J[M]. Handbook of Surface and Nano-metrology(2010).
[115] [2018-05-12]. Crack https:∥baike.baidu.com/item/%E8%A3%82%E7%BA%B9/10776905?fr=aladdin..
[116] Hanbay K, Talu M F, Özgüven O F. Fabric defect detection systems and methods—A systematic literature review[J]. Optik, 127, 11960-11973(2016). http://www.researchgate.net/publication/308909087_Fabric_defect_detection_systems_and_methods-A_systematic_literature_review
[117] Tsai D M, Hung C Y. Automatic defect inspection of patterned thin film transistor-liquid crystal display (TFT-LCD) panels using one-dimensional Fourier reconstruction and wavelet decomposition[J]. International Journal of Production Research, 43, 4589-4607(2005).
[118] Tsai D M, Chuang S T, Tseng Y H. One-dimensional-based automatic defect inspection of multiple patterned TFT-LCD panels using Fourier image reconstruction[J]. International Journal of Production Research, 45, 1297-1321(2007).
[119] Zhang T D, Lu R S. Automatic period selection for DFT method in the application of TFT-LCD panel detection[J]. Journal of Electronic Measurement and Instrumentation, 30, 361-373(2016).
[120] Zhang T D, Lu R S, Dang X M. Automatic neighbor r selection for one-dimensional DFT method in the surface defect inspection of TFT-LCD[J]. China Mechanical Engineering, 27, 2895-2901(2016).
[121] Zhang T D, Lu R S. Surface defect inspection of TFT-LCD panels based on 1D Fourier method[J]. Proceedings of SPIE, 9903, 990308(2016). http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2485222
[122] Zhang T D, Lu R S, Zhang S Z. Surface defect inspection of TFT-LCD panels based on 2D DFT[J]. Opto-Electronic Engineering, 43, 7-15(2016).
[123] Tsai D M, Hsieh C Y. Automated surface inspection for directional textures[J]. Image and Vision Computing, 18, 49-62(1999). http://www.sciencedirect.com/science/article/pii/S0262885699000098
[124] Bissi L, Baruffa G, Placidi P et al. Automated defect detection in uniform and structured fabrics using Gabor filters and PCA[J]. Journal of Visual Communication and Image Representation, 24, 838-845(2013). http://www.sciencedirect.com/science/article/pii/S1047320313001119
[125] Tsa D M, Wu S K. Automated surface inspection using gabor filters[J]. International Journal of Advanced Manufacturing Technology, 16, 474-482(2000). http://link.springer.com/article/10.1007/s001700070055
[126] Tsai D M, Lin C P. Fast defect detection in textured surfaces using 1D gabor filters[J]. The International Journal of Advanced Manufacturing Technology, 20, 664-675(2002). http://link.springer.com/article/10.1007/s001700200205
[127] Tsai D M, Chiang C H. Automatic band selection for wavelet reconstruction in the application of defect detection[J]. Image and Vision Computing, 21, 413-431(2003). http://www.sciencedirect.com/science/article/pii/S0262885603000039
[128] Tsai D M, Hsiao B. Automatic surface inspection using wavelet reconstruction[J]. Pattern Recognition, 34, 1285-1305(2001). http://www.sciencedirect.com/science/article/pii/S0031320300000716
[129] Perng D B, Chen S H. Directional textures auto-inspection using discrete cosine transform[J]. International Journal of Production Research, 49, 7171-7187(2011). http://www.tandfonline.com/doi/full/10.1080/00207543.2010.495087
[130] Chen S H, Perng D B. Automatic surface inspection for directional textures using discrete cosine transform. [C]∥2009 Chinese Conference on Pattern Recognition, November 04-06, 2009, Nanjing, China. New York: IEEE, 1-5(2009).
[131] Lu C J, Tsai D M. Independent component analysis-based defect detection in patterned liquid crystal display surfaces[J]. Image and Vision Computing, 26, 955-970(2008).
[132] Yao M H, Li J, Wang X B. Solar cells surface defects detection using RPCA method[J]. Chinese Journal of Computers, 36, 1943-1952(2013).
[133] Cen Y G, Zhao R Z, Cen L H et al. Defect inspection for TFT-LCD images based on the low-rank matrix reconstruction[J]. Neurocomputing, 149, 1206-1215(2015). http://www.sciencedirect.com/science/article/pii/S0925231214011631
[134] Lu C J, Tsai D M. Automatic defect inspection for LCDs using singular value decomposition[J]. The International Journal of Advanced Manufacturing Technology, 25, 53-61(2005). http://link.springer.com/article/10.1007/s00170-003-1832-6
[135] Lu C J, Tsai D M. Defect inspection of patterned thin film transistor-liquid crystal display panels using a fast sub-image-based singular value decomposition[J]. International Journal of Production Research, 42, 4331-4351(2004).
[136] Wang X B, Li J, Yao M H et al. Solar cells surface defects detection based on deep learning[J]. Pattern Recognition and Artificial Intelligence, 27, 517-523(2014).
[137] Liu Z F, Yan L, Li C L et al. Fabric defect detection algorithm based on sparse optimization[J]. Journal of Textile Research, 37, 56-61, 74(2016).
[138] Gong F, Zhang X W, Sun H. Detection system for solar module surface defects based on constrained ICA model and PSO method[J]. Acta Optica Sinica, 32, 0415002(2012).
[139] He L F, Ren X W, Gao Q H et al. The connected-component labeling problem: A review of state-of-the-art algorithms[J]. Pattern Recognition, 70, 25-43(2017). http://www.sciencedirect.com/science/article/pii/S0031320317301693
[140] Connected component labeling[EB/OL]. -06-13)[2018-05-10]. https:∥blogs.math works.com/steve/category/connected-components/.(2007).
[141] GC image user’s guide[EB/OL]. -07-18)[2018-05-10]. http:∥cse.unl.edu/~reich/ gcimage/V1.3/analysis.html.(2007).
[142] Wang C S, Lu R S, Li Q et al. Mark defects based on hotelling transform and CUDA architecture[J]. Science Technology and Engineering, 12, 2556-2560(2012).
[143] Borghese N A, Fomasi M. Automatic defect classification on a production line[J]. Intelligent Industrial Systems, 1, 373-393(2015). http://link.springer.com/article/10.1007/s40903-015-0018-5/fulltext.html
[144] Chiu Y S P, Lin H D. A hybrid approach based on hotelling statistics for automated visual inspection of display blemishes in LCD panels[J]. Expert Systems with Applications, 36, 12332-12339(2009).
[145] Sánchez-Marín F J. Automatic recognition of biological shapes with and without representations of shape[J]. Artificial Intelligence in Medicine, 18, 173-186(2000). http://europepmc.org/abstract/MED/10648849
[146] Zhou S Y, Chen Y P, Zhang D L et al. Classification of surface defects on steel sheet using convolutional neural networks[J]. Materiali in Tehnologije, 51, 123-131(2017). http://www.researchgate.net/publication/313896846_Classification_of_surface_defects_on_steel_sheet_using_convolutional_neural_networks
[147] Vapnik V N. The nature of statistical learning theory[M]. Berlin: Springer(1999).
[148] Vapnik V N. Estimation of dependences based on empirical data[M]. 2nd ed. Berlin: Springer(2006).
[149] Jia H, Murphey Y L, Shi J et al. An intelligent real-time vision system for surface defect detection. [C]∥Proceedings of the 17th International Conference on Pattern Recognition, August 26, 2004, Cambridge, UK. New York: IEEE, 239-242(2004).
[150] Akbar H, Suryana N, Akbar F. Surface defect detection and classification based on statistical filter and decision tree[J]. International Journal of Computer Theory and Engineering, 774-779(2013). http://www.researchgate.net/publication/272912419_Surface_Defect_Detection_and_Classification_Based_on_Statistical_Filter_and_Decision_Tree
[151] Tang B, Kong J Y, Wu S Q. Review of surface defect detection based on machine vision[J]. Journal of Image and Graphics, 22, 1640-1663(2017).
[152] Ravikumar S, Ramachandran K I, Sugumaran V. Machine learning approach for automated visual inspection of machine components[J]. Expert Systems with Applications, 38, 3260-3266(2011). http://dl.acm.org/citation.cfm?id=1922786
[153] Weiss K, Khoshgoftaar T M, Wang D D. A survey of transfer learning[J]. Journal of Big Data, 3, 1-40(2016). http://link.springer.com/article/10.1186/s40537-016-0043-6
[154] Islam S M S, Rahman S, Dey E K. Application of deep learning to computer vision: a comprehensive study. [C]∥2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), May 13-14, 2016, Dhaka, Bangladesh, 592-597(2016).
[155] Wang J J, Ma Y L, Zhang L B et al. 2018-05-12]. https:∥www.sciencedirect.com/science/article/pii/S0278612518300037.(2018).
[156] Kamilaris A. Prenafeta-Boldú F X. Deep learning in agriculture: a survey[J]. Computers and Electronics in Agriculture, 147, 70-90(2018).
[157] Khan S, Yairi T. A review on the application of deep learning in system health management[J]. Mechanical Systems and Signal Processing, 107, 241-265(2018). http://www.sciencedirect.com/science/article/pii/S0888327017306064
[158] Nguyen V N, Jenssen R J, Roverso D. Automatic autonomous vision-based power line inspection: a review of current status and the potential role of deep learning[J]. International Journal of Electrical Power & Energy Systems, 99, 107-120(2018).
[159] Huang H W, Li Q T, Zhang D M. Deep learning based image recognition for crack and leakage defects of metro shield tunnel[J]. Tunnelling and Underground Space Technology, 77, 166-176(2018). http://www.sciencedirect.com/science/article/pii/S0886779817310258
[160] Xing F Y, Xie Y P, Su H et al. Deep learning in microscopy image analysis: a survey[J]. IEEE Transactions on Neural Networks and Learning Systems, 1-19(2017). http://ieeexplore.ieee.org/document/8118310/
[161] Krig S. Computer vision metrics, survey, taxonomy and analysis[M]. Berlin: Springer(2016).
[162] Li D. A tutorial survey of architectures, algorithms, and applications for deep learning[J]. APSIPA Transactions on Signal & Information Processing, 3, 1-29(2014). http://journals.cambridge.org/abstract_S2048770313000097
[163] Park J K, Kwon B K, Park J H et al. Machine learning-based imaging system for surface defect inspection[J]. International Journal of Precision Engineering and Manufacturing-Green Technology, 3, 303-310(2016). http://link.springer.com/article/10.1007/s40684-016-0039-x
[164] Voulodimos A, Doulamis N, Doulamis A et al. Deep learning for computer vision: a brief review[J]. Computational Intelligence and Neuroscience, 2018, 1-13(2018). http://www.ncbi.nlm.nih.gov/pubmed/29487619
[165] Ren R X, Hung T, Tan K C. A generic deep-learning-based approach for automated surface inspection[J]. IEEE Transactions on Cybernetics, 48, 929-940(2018). http://ieeexplore.ieee.org/document/7864335/
[166] Król D, Nguyen N T, Shirai K. Advanced topics in intelligent information and database systems[M]. Berlin: Springer, 235-247(2017).
[167] Cha Y J, Choi W, Büyüköztürk O. Deep learning-based crack damage detection using convolutional neural networks[J]. Computer-Aided Civil and Infrastructure Engineering, 32, 361-378(2017). http://onlinelibrary.wiley.com/doi/10.1111/mice.12263/full
[168] Atha D J, Jahanshahi M R. Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection[J]. Structural Health Monitoring: An International Journal, 1475921717737051(2017).
[169] Yu Z, Wu X, Gu X. Fully convolutional networks for surface defect inspection in industrial environment. [C]∥Liu M, Chen H, Vincze M. Computer Vision Systems. Lecture Notes in Computer Science. Cham: Springer, 10528, 417-426(2017).
[170] Ye R F, Pan C S, Chang M et al. Intelligent defect classification system based on deep learning[J]. Advances in Mechanical Engineering, 10, 168781401876668(2018). http://www.researchgate.net/publication/324035205_Intelligent_defect_classification_system_based_on_deep_learning
[171] Zhang M, Wu J L, Lin H F et al. The application of one-class classifier based on CNN in image defect detection[J]. Procedia Computer Science, 114, 341-348(2017). http://www.researchgate.net/publication/320365729_The_Application_of_One-Class_Classifier_Based_on_CNN_in_Image_Defect_Detection
