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Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

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Abstract

High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation.

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No datasets were generated or analysed during the current study.

References

  1. Cioffi R, Travaglioni M, Piscitelli G, Petrillo A, De Felice F (2020) Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability 12(2):492

    Article  Google Scholar 

  2. Gabsi AEH (2024) Integrating artificial intelligence in Industry 4.0: Insights, challenges, and future prospects–a literature review. Ann Oper Res 1–28

  3. Lee JH, Kim BH, Kim MY (2021) Machine learning-based automatic optical inspection system with multimodal optical image fusion network. Int J Control Autom Syst 19:3503–3510

    Article  Google Scholar 

  4. Tong X, Yu Z, Tian X, Ge H, Wang X (2022) Improving accuracy of automatic optical inspection with machine learning. Front Comp Sci 16:1–12

    Google Scholar 

  5. Rasheed A, Zafar B, Rasheed A, Ali N, Sajid M, Dar SH, Habib U, Shehryar T, Mahmood MT (2020) Fabric defect detection using computer vision techniques: A comprehensive review. Math Probl Eng 2020:1–24

    Article  Google Scholar 

  6. Jiang Y, Li C, Zhang X, Wang J, Liu C (2021) Surface defect detection of high precision cylindrical metal parts based on machine vision. Intelligent Robotics and Applications: 14th International Conference, ICIRA 2021, Yantai, China, October 22–25, 2021. Proceedings, Part II 14:810–820

    Google Scholar 

  7. Ye M, Zhang W, Cui G, Wang X (2022) Surface defects inspection of cylindrical metal workpieces based on weakly supervised learning. Int J Adv Manuf Tech 1–17

  8. Qi J, Xu M, Zhang W, Liu Y, Dai X (2022) Defect detection of pipeline inner surface based on coaxial digital image correlation with hypercentric lens. Materials 15(21):7543. https://doi.org/10.3390/ma15217543

    Article  Google Scholar 

  9. Zheng S, Lu J, Zhao H, Zhu X, Luo Z, Wang Y, Fu Y, Feng J, Xiang T, Torr PH (2021) Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 6881–6890

  10. Zheng X, Zheng S, Kong Y, Chen J (2021) Recent advances in surface defect inspection of industrial products using deep learning techniques. The International Journal of Advanced Manufacturing Technology 113:35–58

    Article  Google Scholar 

  11. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90. https://doi.org/10.1145/3065386

    Article  Google Scholar 

  12. Chow LS, Tang GS, Solihin MI, Gowdh NM, Ramli N, Rahmat K (2023) Quantitative and qualitative analysis of 18 deep convolutional neural network (CNN) models with transfer learning to diagnose COVID-19 on Chest X-ray (CXR) Images. SN Comput Sci 4(2):141

    Article  Google Scholar 

  13. Mun NW, Solihin MI, Chow LS, Machmudah A (2022) Pneumonia identification from chest X-rays (CXR) using ensemble deep learning approach. Proceedings of the 6th International Conference on Electrical, Control and Computer Engineering: InECCE2021, Kuantan, Pahang, Malaysia, 23rd August, 1139–1151

  14. Wadekar SP et al (2023) An optimized deep learning model for automatic diagnosis of COVID-19 using chest X-Ray images. Lect Notes Electr Eng 988: 61–73. https://link.springer.com/chapter/10.1007/978-981-19-8703-8_6

  15. Czimmermann T, Ciuti G, Milazzo M, Chiurazzi M, Roccella S, Oddo CM, Dario P (2020) Visual-based defect detection and classification approaches for industrial applications—a survey. Sensors 20(5):1459

    Article  Google Scholar 

  16. Bulusu S, Kailkhura B, Li B, Varshney P, Song D (2020) Anomalous instance detection in deep learning: A survey. Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States)

  17. Chalapathy R, Chawla S (2019) Deep learning for anomaly detection: A survey. arXiv Preprint arXiv:1901.03407

  18. Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: A survey. ACM Computing Surveys (CSUR) 41(3):1–58

    Article  Google Scholar 

  19. Liu J, Xie G, Wang J, Li S, Wang C, Zheng F, Jin Y (2023) Deep industrial image anomaly detection: A survey. arXiv Preprint arXiv:2301.11514. 2

  20. Liu T, Cao G-Z, He Z, Xie S (2023) RoIA: Region of interest attention network for surface defect detection. IEEE Trans Semicond Manuf

  21. Pang G, Shen C, Cao L, Hengel AVD (2022) Deep learning for anomaly detection: A review. ACM Comput Surv 54(2):1–38. https://doi.org/10.1145/3439950

    Article  Google Scholar 

  22. Bergmann P, Fauser M, Sattlegger D, Steger C (2019) MVTec AD--a comprehensive real-world dataset for unsupervised anomaly detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 9592–9600

  23. Hao R, Lu B, Cheng Y, Li X, Huang B (2021) A steel surface defect inspection approach towards smart industrial monitoring. J Intell Manuf 32:1833–1843

    Article  Google Scholar 

  24. Yu Z, Wu X, Gu X (2017) Fully convolutional networks for surface defect inspection in industrial environment. Computer Vision Systems: 11th International Conference, ICVS 2017, Shenzhen, China, July 10–13, 2017. Revised Selected Papers 11:417–426

    Google Scholar 

  25. Zhou A, Ai B, Qu P, Shao W (2021) Defect detection for highly reflective rotary surfaces: An overview. Meas Sci Technol 32(6):062001. https://doi.org/10.1088/1361-6501/abd579

    Article  Google Scholar 

  26. Yuan S, Yan N, Zhu L, Hu J, Li Z, Liu H, Zhang X (2022) High dynamic online detection method for surface defects of small diameter reflective inner wall. Measurement 195:111138. https://doi.org/10.1016/j.measurement.2022.111138

    Article  Google Scholar 

  27. Li W, Solihin MI, Nugroho HA (2024) RCA: YOLOv8-based surface defects detection on the inner wall of cylindrical high-precision parts. Arab J Sci Eng 1–19

  28. Peiner E, Balke M, Doering L (2008) Slender tactile sensor for contour and roughness measurements within deep and narrow holes. IEEE Sens J 8(12):1960–1967. https://doi.org/10.1109/JSEN.2008.2006701

    Article  Google Scholar 

  29. Dong Y, Li J, Ren Y, Fan S, Zhao S (2020) Laser-assisted cyclic chipless splitting for hard-to-cut thick wall tubes and fatigue fracture mechanism analysis. Int J Mech Sci 168:105308. https://doi.org/10.1016/j.ijmecsci.2019.105308

    Article  Google Scholar 

  30. Zuo B, Wang F (2016) Surface cutting defect detection of magnet using Fourier image reconstruction. Comput Eng Appl 52(3):256–260

    Google Scholar 

  31. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 1, I-511-I–518. https://doi.org/10.1109/CVPR.2001.990517

  32. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 1, 886–893

  33. Xue-Wu Z, Yan-Qiong D, Yan-Yun L, Ai-Ye S, Rui-Yu L (2011) A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM. Expert Syst Appl 38(5):5930–5939

    Article  Google Scholar 

  34. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139

    Article  Google Scholar 

  35. Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: Towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28

  36. Bochkovskiy A, Wang C-Y, Liao H-YM (2020) Yolov4: Optimal speed and accuracy of object detection. arXiv Preprint arXiv:2004.10934

  37. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 779–788

  38. Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv Preprint arXiv:1804.02767

  39. Wang C-Y, Bochkovskiy A, Liao H-YM (2023) YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 7464–7475

  40. Wang J, Xu G, Yan F, Wang J, Wang Z (2023) Defect transformer: An efficient hybrid transformer architecture for surface defect detection. Measurement 211:112614

    Article  Google Scholar 

  41. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) SSD: Single shot multibox detector (Vol. 9905, pp. 21–37). https://doi.org/10.1007/978-3-319-46448-0_2

  42. Benmoussat M, Spinnler K, Guillaume M (2012) Surface defect detection of metal parts: Use of multimodal illuminations and hyperspectral imaging algorithms. IEEE International Conference on Imaging Systems and Techniques Proceedings 2012:228–233

    Article  Google Scholar 

  43. Khatyreva A, Kuntz I, Schmid-Schirling T, Brox T, Carl D (2023) Unsupervised anomaly detection for industrial manufacturing using multiple perspectives of free falling parts. Automated Visual Inspection and Machine Vision V 12623:101–114

    Google Scholar 

  44. Dong H, Song K, He Y, Xu J, Yan Y, Meng Q (2019) PGA-Net: Pyramid feature fusion and global context attention network for automated surface defect detection. IEEE Trans Industr Inf 16(12):7448–7458

    Article  Google Scholar 

  45. Yun JP, Shin WC, Koo G, Kim MS, Lee C, Lee SJ (2020) Automated defect inspection system for metal surfaces based on deep learning and data augmentation. J Manuf Syst 55:317–324

    Article  Google Scholar 

  46. Kim Y, Lee J-S, Lee J-H (2023) Automatic defect classification using semi-supervised learning with defect localization. IEEE Trans Semicond Manuf

  47. Qiu F, Gao Z, Xia X, Lo D, Grundy J, Wang X (2021) Deep just-in-time defect localization. IEEE Trans Software Eng 48(12):5068–5086

    Google Scholar 

  48. Saberironaghi A, Ren J, El-Gindy M (2023) Defect detection methods for industrial products using deep learning techniques: A review. Algorithms 16(2):95. https://doi.org/10.3390/a16020095

    Article  Google Scholar 

  49. Li S, Song B, Liang R (2022) Structured light dark-field microscope. arXiv Preprint arXiv:2202.05357

  50. Li Z, Tian X, Liu X, Liu Y, Shi X (2022) A two-stage industrial defect detection framework based on improved-yolov5 and optimized-inception-resnetv2 models. Appl Sci 12(2):834

    Article  Google Scholar 

  51. Wang JL, Qu XH, Zhao Y (2009) Design of lighting system in multi vision detection. Electro-Optic Technology Application 24(4):1–5

    Google Scholar 

  52. Li Y, Wang S, Tian Q, Ding X (2015) A survey of recent advances in visual feature detection. Neurocomputing 149:736–751

    Article  Google Scholar 

  53. Chertov AN, Gorbunova EV, Korotaev VV, Peretyagin VS (2014) Solution of multi-element LED light sources development automation problem. Thirteenth International Conference on Solid State Lighting 9190:204–212

    Google Scholar 

  54. Liu Y, Xu K, Xu J (2019) An improved MB-LBP defect recognition approach for the surface of steel plates. Appl Sci 9(20):4222

    Article  Google Scholar 

  55. Moreno I, Avendaño-Alejo M, Tzonchev RI (2006) Designing light-emitting diode arrays for uniform near-field irradiance. Appl Opt 45(10):2265–2272

    Article  Google Scholar 

  56. Zhong Q, Zhang X, Chen Z (2014) A fast coplanarity inspection system for double-sides IC leads using single viewpoint. Intelligent Robotics and Applications: 7th International Conference, ICIRA 2014, Guangzhou, China, December 17–20, 2014. Proceedings, Part II 7:216–225

    Google Scholar 

  57. Shamkhalichenar H, Bueche CJ, Choi J-W (2020) Printed circuit board (PCB) technology for electrochemical sensors and sensing platforms. Biosensors 10(11):159

    Article  Google Scholar 

  58. Albeanu DF, Soucy E, Sato TF, Meister M, Murthy VN (2008) LED arrays as cost effective and efficient light sources for widefield microscopy. PLoS ONE 3(5):e2146

    Article  Google Scholar 

  59. Xu Y, Wang D, Duan B, Yu H, Liu H (2021) Copper strip surface defect detection model based on deep convolutional neural network. Appl Sci 11(19):8945

    Article  Google Scholar 

  60. Braun D, Heeger AJ (1991) Visible light emission from semiconducting polymer diodes. Appl Phys Lett 58(18):1982–1984

    Article  Google Scholar 

  61. Ryer A (1997) Light measurement handbook

  62. Rogalski A (2011) Recent progress in infrared detector technologies. Infrared Phys Technol 54(3):136–154

    Article  Google Scholar 

  63. Al-Mallahi A, Kataoka T, Okamoto H, Shibata Y (2010) Detection of potato tubers using an ultraviolet imaging-based machine vision system. Biosys Eng 105(2):257–265

    Article  Google Scholar 

  64. Connolly C (2008) X-ray systems for security and industrial inspection. Sens Rev 28(3):194–198

    Article  Google Scholar 

  65. Rocha H, Peretta IS, Lima GFM, Marques LG, Yamanaka K (2016) Exterior lighting computer-automated design based on multi-criteria parallel evolutionary algorithm: Optimized designs for illumination quality and energy efficiency. Expert Syst Appl 45:208–222

    Article  Google Scholar 

  66. Ma X, Qi P, Wu Z, Yang Y, Li C, Zhong J (2023) Image contrast enhancement by using LED annular oblique illumination in bright-field microscopy. Photonics 10(4):404

    Article  Google Scholar 

  67. Mehta S, Patel A, Mehta J (2015) CCD or CMOS image sensor for photography. International Conference on Communications and Signal Processing (ICCSP) 2015:0291–0294

    Google Scholar 

  68. Dworkin SB, Nye TJ (2006) Image processing for machine vision measurement of hot formed parts. J Mater Process Technol 174(1–3):1–6

    Article  Google Scholar 

  69. Nehir M, Frank C, Aßmann S, Achterberg EP (2019) Improving optical measurements: Non-linearity compensation of compact charge-coupled device (CCD) spectrometers. Sensors 19(12):2833

    Article  Google Scholar 

  70. Sun B, Yuan N, Zhao Z (2019) A hybrid demosaicking algorithm for area scan industrial camera based on fuzzy edge strength and residual interpolation. IEEE Trans Industr Inf 16(6):4038–4048

    Article  Google Scholar 

  71. Steger C, Ulrich M (2021) A camera model for line-scan cameras with telecentric lenses. Int J Comput Vision 129(1):80–99

    Article  Google Scholar 

  72. Steger C, Ulrich M (2022) A multi-view camera model for line-scan cameras with telecentric lenses. J Math Imaging Vis 64(2):105–130

    Article  Google Scholar 

  73. Zhang B, Yang T, Song W, Li F, Liu M (2019) Detection of tiny cylindrical end face defects based on double structure light. J Zhengzhou Univ(Nat Sci Ed) 51(4):11–15

    Google Scholar 

  74. Pierer A, Hauser M, Hoffmann M, Naumann M, Wiener T (2022) Inline quality monitoring of reverse extruded aluminum parts with cathodic dip-paint coating (KTL)

  75. Perez-Cortes J-C, Perez AJ, Saez-Barona S, Guardiola J-L, Salvador I (2018) A system for in-line 3D inspection without hidden surfaces. Sensors 18(9):2993

    Article  Google Scholar 

  76. Bao T, Chen J, Li W, Wang X, Fei J, Wu L, Zhao R, Zheng Y (2022) MIAD: A maintenance inspection dataset for unsupervised anomaly detection. arXiv Preprint arXiv:2211.13968

  77. Bao Y, Wu L, Dai Y, Zhao Y, Wei S (2022) Research on surface image acquisition system of train bearing cylindrical roller. J Mech Sci Technol 36(9):4353–4361

    Article  Google Scholar 

  78. Hansen P, Alismail H, Rander P, Browning B (2015) Visual mapping for natural gas pipe inspection. Int J Robot Res 34(4–5):532–558

    Article  Google Scholar 

  79. Yan S, Qi J, Zhao NZ, Cheng Y, Qi SWJ (2014) Multiple crack detection of pipes using PZT-based guided waves. Appl Mech Mater 448:3702–3708

    Google Scholar 

  80. Fuoco R, Moreira M (2010) Fatigue cracks in aluminum cylinder heads for diesel engines. Int J Metalcast 4:19–32

    Article  Google Scholar 

  81. Ortiz AF, Rodriguez SA, Coronado JJ (2013) Failure analysis of the engine cylinder of a training aircraft. Eng Fail Anal 35:686–691

    Article  Google Scholar 

  82. Hong E, Zhang H, Katz R, Agapiou JS (2012) Non-contact inspection of internal threads of machined parts. Int J Adv Manuf Tech 62:221–229

    Article  Google Scholar 

  83. Tsai D-M, Wu S-C, Li W-C (2012) Defect detection of solar cells in electroluminescence images using Fourier image reconstruction. Sol Energy Mater Sol Cells 99:250–262

    Article  Google Scholar 

  84. Behrendt V, Buchta D, Adolph S, Lutz C, Scherer J, Blättermann A (2023) In-line fluorescence detector for production control in robot-driven environments. Optical Measurement Systems for Industrial Inspection XIII 12618:414–421

    Google Scholar 

  85. Liu H, Zhou W, Kuang Q, Cao L, Gao B (2010) Defect detection of IC wafer based on two-dimension wavelet transform. Microelectron J 41(2–3):171–177

    Article  Google Scholar 

  86. Xiangdong GAO, Yilong XIE, Ziqin C, Deyong You (2017) Fractal feature detection of high-strength steel weld defects by magneto optical imaging. China Weld 38(7):1–4

    Google Scholar 

  87. Ren H, Tian K, Hong S, Dong B, Xing F, Qin L (2019) Visualized investigation of defect in cementitious materials with electrical resistance tomography. Constr Build Mater 196:428–436

    Article  Google Scholar 

  88. Li J, Quan X, Wang Y (2020) Research on defect detection algorithm of ceramic tile surface with multi-feature fusion. Comput Eng Appl 56:191–198

    Google Scholar 

  89. Li Y, Liu M (2018) Aerial image classification using color coherence vectors and rotation & uniform invariant LBP descriptors. 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) 653–656

  90. Wang J, Fu P, Gao RX (2019) Machine vision intelligence for product defect inspection based on deep learning and Hough transform. J Manuf Syst 51:52–60

    Article  Google Scholar 

  91. Tsai D-M, Huang C-K (2018) Defect detection in electronic surfaces using template-based Fourier image reconstruction. IEEE Transactions on Components, Packaging and Manufacturing Technology 9(1):163–172

    Article  Google Scholar 

  92. Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vision 57:137–154

    Article  Google Scholar 

  93. Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2009) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645

    Article  Google Scholar 

  94. Felzenszwalb P, McAllester D, Ramanan D (2008) A discriminatively trained, multiscale, deformable part model. IEEE Conference on Computer Vision and Pattern Recognition 2008:1–8

    Google Scholar 

  95. Malisiewicz T, Gupta A, Efros AA (2011) Ensemble of exemplar-SVMS for object detection and beyond. International Conference on Computer Vision 2011:89–96

    Google Scholar 

  96. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv Preprint arXiv:1409.1556

  97. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V,  Rabinovich A (2015) Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1–9

  98. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 770–778

  99. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 4700–4708

  100. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 7132–7141

  101. Zhang X, Zhou X, Lin M, Sun J (2018) ShuffleNet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 6848–6856

  102. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv Preprint arXiv:1704.04861

  103. Wu H, Zhou Z (2021) Using convolution neural network for defective image classification of industrial components. Mob Inf Syst 2021:1–8

    Google Scholar 

  104. Byun Y, Baek J-G (2021) Pattern classification for small-sized defects using multi-head CNN in semiconductor manufacturing. Int J Precis Eng Manuf 22:1681–1691

    Article  Google Scholar 

  105. Deitsch S, Christlein V, Berger S, Buerhop-Lutz C, Maier A, Gallwitz F, Riess C (2019) Automatic classification of defective photovoltaic module cells in electroluminescence images. Sol Energy 185:455–468

    Article  Google Scholar 

  106. Alcantarilla PF, Bartoli A, Davison AJ (2012) KAZE features. Computer Vision–ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7–13, 2012, Proceedings, Part VI 12, 214–227

  107. Lowe DG (1999) Object recognition from local scale-invariant features. Proceedings of the Seventh IEEE International Conference on Computer Vision 2:1150–1157

    Article  Google Scholar 

  108. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110(3):346–359

    Article  Google Scholar 

  109. Shang L, Yang Q, Wang J, Li S, Lei W (2018) Detection of rail surface defects based on CNN image recognition and classification. 2018 20th International Conference on Advanced Communication Technology (ICACT) 45–51

  110. Nagata F, Tokuno K, Nakashima K, Otsuka A, Ikeda T, Ochi H, Watanabe K, Habib MK (2019) Fusion method of convolutional neural network and support vector machine for high accuracy anomaly detection. IEEE International Conference on Mechatronics and Automation (ICMA) 2019:970–975

    Article  Google Scholar 

  111. Xie Q, Li D, Xu J, Yu Z, Wang J (2019) Automatic detection and classification of sewer defects via hierarchical deep learning. IEEE Trans Autom Sci Eng 16(4):1836–1847

    Article  Google Scholar 

  112. Eslami E, Yun H-B (2021) Attention-based multi-scale convolutional neural network (A+ MCNN) for multi-class classification in road images. Sensors 21(15):5137

    Article  Google Scholar 

  113. Lu Y, Ma L, Jiang H (2020) A light CNN model for defect detection of LCD. Frontier Computing: Theory, Technologies and Applications (FC 2019) 8, 10–19

  114. Konovalenko I, Maruschak P, Brezinová J, Prentkovskis O, Brezina J (2022) Research of U-Net-based CNN architectures for metal surface defect detection. Machines 10(5):327

    Article  Google Scholar 

  115. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision 618–626

  116. Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2921–2929

  117. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    Article  Google Scholar 

  118. Sampath V, Maurtua I, Martín JJA, Rivera A, Molina J, Gutierrez A (2023) Attention guided multi-task learning for surface defect identification. IEEE Trans Ind Inform

  119. Zhao Z, Xu G, Qi Y, Liu N, Zhang T (2016) Multi-patch deep features for power line insulator status classification from aerial images. International Joint Conference on Neural Networks (IJCNN) 2016:3187–3194

    Article  Google Scholar 

  120. Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. European Conference on Computer Vision 213–229

  121. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 580–587

  122. He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916

    Article  Google Scholar 

  123. Girshick R (2015) Fast R-CNN. Proc IEEE Int Conf Comput Vis 1440–1448

  124. Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2117–2125

  125. Lin T-Y, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. Proc IEEE Int Conf Comput Vis 2980–2988

  126. Law H, Deng J (2018) CornerNet: detecting objects as paired keypoints. Proceedings of the European Conference on Computer Vision (ECCV) 734–750

  127. Duan K, Bai S, Xie L, Qi H, Huang Q, Tian Q (2019) CenterNet: Keypoint triplets for object detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 6569–6578

  128. Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE/CVF International Conference on Computer Vision 10012–10022

  129. Zhang X, Zhang Y, Hu M, Ju X (2020) Insulator defect detection based on YOLO and SPP-Net. International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE) 2020:403–407

    Google Scholar 

  130. Li K, Wang X, Ji L (2019) Application of multi-scale feature fusion and deep learning in detection of steel strip surface defect. International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) 2019:656–661

    Google Scholar 

  131. Ye Y, Ma K, Zhou H, Arola D, Zhang D (2019) An automated shearography system for cylindrical surface inspection. Measurement 135:400–405

    Article  Google Scholar 

  132. Hu B, Wang J (2020) Detection of PCB surface defects with improved faster-RCNN and feature pyramid network. Ieee Access 8:108335–108345

    Article  Google Scholar 

  133. Tao J, Zhu Y, Jiang F, Liu H, Liu H (2022) Rolling surface defect inspection for drum-shaped rollers based on deep learning. IEEE Sens J 22(9):8693–8700. https://doi.org/10.1109/JSEN.2022.3159743

    Article  Google Scholar 

  134. Yuting S, Hongxing L (2023) A deep learning based dislocation detection method for cylindrical silicon growth process. Appl Intell 53(8):9188–9203. https://doi.org/10.1007/s10489-022-03800-0

    Article  Google Scholar 

  135. Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 8759–8768

  136. Shafi I, Mazahir A, Fatima A, Ashraf I (2022) Internal defects detection and classification in hollow cylindrical surfaces using single shot detection and MobileNet. Measurement 202:111836. https://doi.org/10.1016/j.measurement.2022.111836

    Article  Google Scholar 

  137. Li Y, Huang H, Xie Q, Yao L, Chen Q (2018) Research on a surface defect detection algorithm based on MobileNet-SSD. Appl Sci 8(9):1678

    Article  Google Scholar 

  138. Zhu X, Su W, Lu L, Li B, Wang X, Dai J (2020) Deformable DETR: Deformable transformers for end-to-end object detection. arXiv Preprint arXiv:2010.04159

  139. Zhang L, Yan S, Hong J, Xie Q, Zhou F, Ran S (2023) An improved defect recognition framework for casting based on DETR algorithm. J Iron Steel Res Int 30(5):949–959

    Article  Google Scholar 

  140. Zhang Z, Zhao Z, Zhang X, Sun C, Chen X (2023) Industrial anomaly detection with domain shift: A real-world dataset and masked multi-scale reconstruction. arXiv Preprint arXiv:2304.02216

  141. Xu W, Zhong X, Luo M, Weng L, Zhou G (2022) End-to-end insulator string defect detection in a complex background based on a deep learning model. Front Energy Res 10:928162

    Article  Google Scholar 

  142. Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PH (2016) Fully-convolutional Siamese networks for object tracking. Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8–10 and 15–16, 2016. Proceedings, Part II 14:850–865

    Google Scholar 

  143. Luan C, Cui R, Sun L, Lin Z (2020) A Siamese network utilizing image structural differences for cross-category defect detection. IEEE International Conference on Image Processing (ICIP) 2020:778–782

    Google Scholar 

  144. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 3431–3440

  145. Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495

    Article  Google Scholar 

  146. Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015. Proceedings, Part III 18:234–241

    Google Scholar 

  147. Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2014) Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv Preprint arXiv:1412.7062

  148. Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848

    Article  Google Scholar 

  149. Chen L-C, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv Preprint arXiv:1706.05587

  150. Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV) 801–818

  151. Jégou S, Drozdzal M, Vazquez D, Romero A, Bengio Y (2017) The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops 11–19

  152. Paszke A, Chaurasia A, Kim S, Culurciello E (2016) ENet: A deep neural network architecture for real-time semantic segmentation. arXiv Preprint arXiv:1606.02147

  153. Chaurasia A, Culurciello E (2017) LinkNet: Exploiting encoder representations for efficient semantic segmentation. IEEE Visual Communications and Image Processing (VCIP) 2017:1–4

    Google Scholar 

  154. Lin G, Milan A, Shen C, Reid I (2017) RefineNet: Multi-path refinement networks for high-resolution semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1925–1934

  155. Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2881–2890

  156. Martin D, Heinzel S, von Bischhoffshausen JK, Kühl N (2021) Deep learning strategies for industrial surface defect detection systems. arXiv Preprint arXiv:2109.11304

  157. He K, Gkioxari G, Dollár P, Girshick R (2017) Mask R-CNN. Proc IEEE Int Conf Comput Vis 2961–2969

  158. Zhu H, Wang Y, Fan J (2022) IA-Mask R-CNN: Improved anchor design mask R-CNN for surface defect detection of automotive engine parts. Appl Sci 12(13):6633

    Article  Google Scholar 

  159. Dey B, Goswami D, Halder S, Khalil K, Leray P, Bayoumi MA (2022) Deep learning-based defect classification and detection in SEM images. Metrology, Inspection, and Process Control XXXVI, PC120530Y

  160. Hinton GE, Zemel R (1993) Autoencoders, minimum description length and Helmholtz free energy. Adv Neural Inf Process Syst 6

  161. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27

  162. Mohammadi MG, Mahmoud D, Elbestawi M (2021) On the application of machine learning for defect detection in L-PBF additive manufacturing. Opt Laser Technol 143:107338

    Article  Google Scholar 

  163. Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv Preprint arXiv:1312.6114

  164. Principi E, Rossetti D, Squartini S, Piazza F (2019) Unsupervised electric motor fault detection by using deep autoencoders. IEEE/CAA J Autom Sin 6(2):441–451

    Article  Google Scholar 

  165. Youkachen S, Ruchanurucks M, Phatrapomnant T, Kaneko H (2019) Defect segmentation of hot-rolled steel strip surface by using convolutional auto-encoder and conventional image processing. 2019 10th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES) 1–5

  166. Zhai W, Zhu J, Cao Y, Wang Z (2018) A generative adversarial network based framework for unsupervised visual surface inspection. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 1283–1287

  167. He L, Shi N, Malik K, Li F (2022) Unsupervised defect inspection algorithm based on cascaded GAN with edge repair feature fusion. Appl Intell 52(2):2051–2069

    Article  Google Scholar 

  168. Ruff L, Vandermeulen R, Goernitz N, Deecke L, Siddiqui SA, Binder A, Müller E, Kloft M (2018) Deep one-class classification. Int Conf Mach Learn 4393–4402

  169. Schlegl T, Seeböck P, Waldstein SM, Schmidt-Erfurth U, Langs G (2017) Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. International Conference on Information Processing in Medical Imaging 146–157

  170. Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv Preprint arXiv:1511.06434

  171. Schlegl T, Seeböck P, Waldstein SM, Langs G, Schmidt-Erfurth U (2019) F-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks. Med Image Anal 54:30–44

    Article  Google Scholar 

  172. Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) Improved training of Wasserstein gans. Adv Neural Inf Process Syst 30

  173. Di H, Ke X, Peng Z, Dongdong Z (2019) Surface defect classification of steels with a new semi-supervised learning method. Opt Lasers Eng 117:40–48

    Article  Google Scholar 

  174. Yang M, Wu P, Feng H (2023) MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities. Eng Appl Artif Intell 119:105835

    Article  Google Scholar 

  175. Chattopadhay A, Sarkar A, Howlader P, Balasubramanian VN (2018) Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. IEEE Winter Conference on Applications of Computer Vision (WACV) 2018:839–847

    Article  Google Scholar 

  176. Niu S, Lin H, Niu T, Li B, Wang X (2019) DefectGAN: Weakly-supervised defect detection using generative adversarial network. 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) 127–132

  177. Zhu J-Y, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. Proc IEEE Int Conf Comput Vis 2223–2232

  178. Venkataramanan S, Peng K-C, Singh RV, Mahalanobis A (2020) Attention guided anomaly localization in images. European Conference on Computer Vision 485–503

  179. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) ImageNet: A large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition 2009:248–255

    Google Scholar 

  180. Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The Pascal visual object classes (VOC) challenge. Int J Comput Vision 88:303–338

    Article  Google Scholar 

  181. Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft COCO: Common objects in context. Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6–12, 2014. Proceedings, Part V 13:740–755

    Google Scholar 

  182. Song K, Yan Y (2013) A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Appl Surf Sci 285:858–864

    Article  Google Scholar 

  183. Tabernik D, Šela S, Skvarč J, Skočaj D (2020) Segmentation-based deep-learning approach for surface-defect detection. J Intell Manuf 31(3):759–776

    Article  Google Scholar 

  184. Božič J, Tabernik D, Skočaj D (2021) Mixed supervision for surface-defect detection: From weakly to fully supervised learning. Comput Ind 129:103459

    Article  Google Scholar 

  185. Bergmann P, Batzner K, Fauser M, Sattlegger D, Steger C (2022) Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization. Int J Comput Vision 130(4):947–969

    Article  Google Scholar 

  186. Mishra P, Verk R, Fornasier D, Piciarelli C, Foresti GL (2021) VT-ADL: A vision transformer network for image anomaly detection and localization. 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) 01–06

  187. Zou Y, Jeong J, Pemula L, Zhang D, Dabeer O (2022) Spot-the-difference self-supervised pre-training for anomaly detection and segmentation. European Conference on Computer Vision 392–408

  188. Tang S, He F, Huang X, Yang J (2019) Online PCB defect detector on a new PCB defect dataset. arXiv Preprint arXiv:1902.06197

  189. Lv X, Duan F, Jiang J, Fu X, Gan L (2020) Deep metallic surface defect detection: The new benchmark and detection network. Sensors 20(6):1562

    Article  Google Scholar 

  190. Xu Y, Arai S, Tokuda F, Kosuge K (2020) A convolutional neural network for point cloud instance segmentation in cluttered scene trained by synthetic data without color. IEEE Access 8:70262–70269

    Article  Google Scholar 

  191. Huang Y, Qiu C, Yuan K (2020) Surface defect saliency of magnetic tile. Vis Comput 36:85–96

    Article  Google Scholar 

  192. Yang D, Cui Y, Yu Z, Yuan H (2021) Deep learning based steel pipe weld defect detection. Appl Artif Intell 35(15):1237–1249

    Article  Google Scholar 

  193. Tao X, Zhang D, Wang Z, Liu X, Zhang H, Xu D (2018) Detection of power line insulator defects using aerial images analyzed with convolutional neural networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems 50(4):1486–1498

    Article  Google Scholar 

  194. Carrera D, Manganini F, Boracchi G, Lanzarone E (2016) Defect detection in SEM images of nanofibrous materials. IEEE Trans Industr Inf 13(2):551–561

    Article  Google Scholar 

  195. Mery D, Riffo V, Zscherpel U, Mondragón G, Lillo I, Zuccar I, Lobel H, Carrasco M (2015) GDXray: The database of X-ray images for nondestructive testing. J Nondestr Eval 34(4):42

    Article  Google Scholar 

  196. Pramerdorfer C, Kampel M (2015) A dataset for computer-vision-based PCB analysis. 2015 14th IAPR International Conference on Machine Vision Applications (MVA) 378–381

  197. Ding R, Dai L, Li G, Liu H (2019) TDD-Net: A tiny defect detection network for printed circuit boards. CAAI Transactions on Intelligence Technology 4(2):110–116

    Article  Google Scholar 

  198. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30

  199. Choromanski K, Likhosherstov V, Dohan D, Song X, Gane A, Sarlos T, Hawkins P, Davis J, Mohiuddin A, Kaiser L (2020) Rethinking attention with performers. arXiv Preprint arXiv:2009.14794

  200. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv Preprint arXiv:2010.11929

  201. Liu Q, Huang X, Shao X, Hao F (2022) Industrial cylinder liner defect detection using a transformer with a block division and mask mechanism. Sci Rep 12(1):10689

    Article  Google Scholar 

  202. Xie X, Liu H, Na Z, Luo X, Wang D, Leng B (2021) DPiT: Detecting defects of photovoltaic solar cells with image transformers. IEEE Access 9:154292–154303

    Article  Google Scholar 

  203. Matsoukas C, Haslum JF, Söderberg M, Smith K (2021) Is it time to replace cnns with transformers for medical images? arXiv Preprint arXiv:2108.09038

  204. Chen P, Liu S, Zhao H, Jia J (2020) Gridmask data augmentation. arXiv Preprint arXiv:2001.04086

  205. DeVries T, Taylor GW (2017) Improved regularization of convolutional neural networks with cutout. arXiv Preprint arXiv:1708.04552

  206. Zhong Z, Zheng L, Kang G, Li S, Yang Y (2020) Random erasing data augmentation. Proceedings of the AAAI Conference on Artificial Intelligence 34(07):13001–13008

    Article  Google Scholar 

  207. Yun S, Han D, Oh SJ, Chun S, Choe J, Yoo Y (2019) CutMix: Regularization strategy to train strong classifiers with localizable features. Proceedings of the IEEE/CVF International Conference on Computer Vision 6023–6032

  208. Zhang H, Cisse M, Dauphin YN, Lopez-Paz D (2017) Mixup: Beyond empirical risk minimization. arXiv Preprint arXiv:1710.09412

  209. Li W, Zhang H, Wang G, Xiong G, Zhao M, Li G, Li R (2023) Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing. Robot Comput-Integr Manuf 80:102470. https://doi.org/10.1016/j.rcim.2022.102470

    Article  Google Scholar 

  210. Mariani G, Scheidegger F, Istrate R, Bekas C, Malossi C (2018) BAGAN: Data augmentation with balancing GAN. arXiv Preprint arXiv:1803.09655

  211. Antoniou A, Storkey A, Edwards H (2017) Data augmentation generative adversarial networks. arXiv Preprint arXiv:1711.04340

  212. Shrivastava A, Pfister T, Tuzel O, Susskind J, Wang W, Webb R (2017) Learning from simulated and unsupervised images through adversarial training. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2107–2116

  213. Shaham TR, Dekel T, Michaeli T (2019) SinGAN: Learning a generative model from a single natural image. Proceedings of the IEEE/CVF International Conference on Computer Vision 4570–4580

  214. Jain S, Seth G, Paruthi A, Soni U, Kumar G (2022) Synthetic data augmentation for surface defect detection and classification using deep learning. J Intell Manuf 1–14

  215. Sleeman WC IV, Kapoor R, Ghosh P (2022) Multimodal classification: Current landscape, taxonomy and future directions. ACM Comput Surv 55(7):1–31

    Article  Google Scholar 

  216. Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J (2021) Learning transferable visual models from natural language supervision. Int Conf Mach Learn 8748–8763

  217. Lin J, Gong S (2023) GridCLIP: One-stage object detection by grid-level CLIP representation learning. arXiv Preprint arXiv:2303.09252

  218. Lu Y, Liu S, Thiagarajan JJ, Sakla W, Anirudh R (2022) On-the-fly object detection using StyleGAN with CLIP guidance. arXiv Preprint arXiv:2210.16742

  219. McMahan B, Moore E, Ramage D, Hampson S, y Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics 1273–1282

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Authors and Affiliations

  1. School of Computer Science and Technology (School of Software), Guangxi University of Science and Technology, Guangxi, 545006, China

    Li Wei

  2. Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, 56000, Malaysia

    Li Wei, Mahmud Iwan Solihin, Lim Wei Hong & Ang Chun Kit

  3. Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600, Pekan, Pahang, Malaysia

    Sarah ‘Atifah Saruchi

  4. Computer Engineering Department, Automotive and Robotics Engineering Program, BINUS ASO School of Engineering, Bina Nusantara University, Jakarta, Indonesia

    Winda Astuti

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L.W. and M.I.S wrote the main manuscript and text, M.I.S., S.A.S. and L.W.H. contributed manuscript editing, W.A. and A.C.K involved in editing and directing the research scope, all authors reviewed the manuscript.

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Wei, L., Solihin, M.I., Saruchi, S.‘. et al. Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review. Oper. Res. Forum 5, 58 (2024). https://doi.org/10.1007/s43069-024-00337-5

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