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A small defect detection technique for industrial product surfaces based on the EA-YOLO model

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Abstract

The detection of surface defects in industrial products is vital for ensuring product quality. Ensuring real-time performance, while improving the detection accuracy of low-pixel-resolution small defects against background interference poses a significant challenge. To address this, the EA-YOLO model, based on Yolov8, is proposed. It includes three main improvements: replacing C2f (Faster Implementation of CSP Bottleneck with 2 convolutions) with a specially designed C2FN (Faster Implementation of CSP FastNet Block with 2 convolutions Network) in the backbone module to reduce parameters and GFLOPs, while enhancing speed; introducing the Environmental Awareness Dynamic Network (EADN) to prevent the loss of defect information in extreme positions; and using the improved Dynamic Adaptive Fusion Detector (DAF-Detect) for predictions. Case studies with the NEU-DET and PCB-DET datasets show that EA-YOLO achieves a mAP of 81.1 and 97.8%, respectively, improving by 4.3 and 4.1% compared to the baseline model, with reduced parameters, GFLOPs, and increased FPS, demonstrating good robustness and generalization ability.

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No datasets were generated or analyzed during the current study.

References

  1. Yun JP, Choi S, Kim SW (2009) Vision-based defect detection of scale-covered steel billet surfaces. Opt Eng 48:037205-037205–037209

    Article  MATH  Google Scholar 

  2. Zheng X, Chen J, Wang H et al (2021) A deep learning-based approach for the automated surface inspection of copper clad laminate images. Appl Intell 51:1262–1279

    Article  MATH  Google Scholar 

  3. Li Z, Wei X, Hassaballah M et al (2024) A deep learning model for steel surface defect detection. Complex Intell Syst 10(1):885–897

    Article  MATH  Google Scholar 

  4. Liu W, Anguelov D, Erhan D, et al (2016) Ssd: single shot multibox detector//Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, Oct 11–14, 2016, Proceedings, Part I 14. Springer International Publishing, pp 21–37

  5. She Y, Li H (2023) A deep learning based dislocation detection method for cylindrical silicon growth process. Appl Intell 53(8):9188–9203

    Article  MATH  Google Scholar 

  6. Bharati P, Pramanik A (2020) Deep learning techniques—R-CNN to mask R-CNN: a survey. Computational Intelligence in Pattern Recognition: Proceedings of CIPR 2019, pp 657–668

  7. Ren S, He K, Girshick R et al (2016) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149

    Article  MATH  Google Scholar 

  8. Wang M, Zhou Y (2024) Autonomous rail surface defect identification based on an improved one-stage object detection algorithm. J Perform Constr Facil 38(5):04024041

    Article  Google Scholar 

  9. Zhao BT, Chen YR, Jia XF et al (2024) Steel surface defect detection algorithm in complex background scenarios. Measurement 237:115189

    Article  Google Scholar 

  10. Hu H, Zhang Z, Xie Z, et al (2019) Local relation networks for image recognition. Proceedings of the IEEE/CVF international conference on computer vision, pp 3464–3473

  11. Yun J P, Choi S H, Kim S W (2009) Vision-based defect detection of scale-covered steel billet surfaces. Opt Eng 48(3):037205–037205-9.

  12. Wang Y, Xia H, Yuan X et al (2018) Distributed defect recognition on steel surfaces using an improved random forest algorithm with optimal multi-feature-set fusion. Multimed Tools Appl 77:16741–16770

    Article  Google Scholar 

  13. Zhang J, Wang H, Tian Y et al (2020) An accurate fuzzy measure-based detection method for various types of defects on strip steel surfaces. Comput Ind 122:103231

    Article  MATH  Google Scholar 

  14. He Y, Song K, Meng Q, Yan Y (2019) An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Trans Instrum Meas 69:1493–1504

    Article  MATH  Google Scholar 

  15. Cha YJ, Choi W, Suh G et al (2018) Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Comput-Aided Civ Infrastruct Eng 33(9):731–747

    Article  MATH  Google Scholar 

  16. Hou S, Dong B, Wang H et al (2020) Inspection of surface defects on stay cables using a robot and transfer learning. Autom Constr 119:103382

    Article  MATH  Google Scholar 

  17. Xue M, Chen M, Peng D et al (2021) One spatio-temporal sharpening attention mechanism for light-weight YOLO models based on sharpening spatial attention. Sensors 21(23):7949

    Article  MATH  Google Scholar 

  18. Chen W, Huang Z, Mu Q et al (2022) PCB defect detection method based on transformer-YOLO. IEEE Access 10:129480–129489

    Article  MATH  Google Scholar 

  19. Jung H, Rhee J (2022) Application of YOLO and ResNet in heat staking process inspection. Sustainability 14(23):15892

    Article  MATH  Google Scholar 

  20. Li M, Wang H, Wan Z (2022) Surface defect detection of steel strips based on improved YOLOv4. Comput Electr Eng 102:108208

    Article  MATH  Google Scholar 

  21. Xing J, Jia M (2021) A convolutional neural network-based method for workpiece surface defect detection. Measurement 176:109185

    Article  MATH  Google Scholar 

  22. Ying Z, Lin Z, Wu Z et al (2022) A modified-YOLOv5s model for detection of wire braided hose defects. Measurement 190:110683

    Article  MATH  Google Scholar 

  23. Lin T Y, Dollár P, Girshick R, et al (2017) Feature pyramid networks for object detection//Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117–2125

  24. Liu Z, Tang R, Duan G et al (2021) TruingDet: Towards high-quality visual automatic defect inspection for mental surface. Opt Lasers Eng 138:106423

    Article  MATH  Google Scholar 

  25. Dong H, Yuan M, Wang S et al (2023) PHAM-YOLO: a parallel hybrid attention mechanism network for defect detection of meter in substation. Sensors 23(13):6052

    Article  MATH  Google Scholar 

  26. Wang Y, Wang H, Xin Z (2022) Efficient detection model of steel strip surface defects based on YOLO-V7. IEEE Access 10:133936–133944

    Article  MATH  Google Scholar 

  27. Chen J, Kao S, He H, et al (2023) Run, don't walk: chasing higher FLOPS for faster neural networks//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 12021–12031

  28. Dai X, Chen Y, Xiao B, et al (2021) Dynamic head: unifying object detection heads with attentions[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 7373–7382

  29. Zhao C, Shu X, Yan X et al (2023) RDD-YOLO: a modified YOLO for detection of steel surface defects. Measurement 214:112776

    Article  Google Scholar 

  30. Zhang Y, Zhang H, Huang Q et al (2024) DsP-YOLO: an anchor-free network with DsPAN for small object detection of multiscale defects. Expert Syst Appl 241:122669

    Article  Google Scholar 

  31. Jocher G, Chaurasia A, Stoken A, et al (2022) ultralytics/yolov5: v6. 1-tensorrt, tensorflow edge tpu and openvino export and inference. Zenodo

  32. Ge Z, Liu S, Wang F, et al (2021) Yolox: exceeding yolo series in 2021. arxiv preprint arxiv:2107.08430

  33. Wang CY, Bochkovskiy A, Liao HYM (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, pp 7464–7475

  34. Xie W, Sun X, Ma W (2024) A light weight multi-scale feature fusion steel surface defect detection model based on YOLOv8. Meas Sci Technol 35(5):055017

    Article  Google Scholar 

  35. Liu R, Huang M, Gao Z et al (2023) MSC-DNet: an efficient detector with multi-scale context for defect detection on strip steel surface. Measurement 209:112467

    Article  MATH  Google Scholar 

  36. Zhang D, Hao X, Wang D et al (2023) An efficient lightweight convolutional neural network for industrial surface defect detection. Artif Intell Rev 56(9):10651–10677

    Article  MATH  Google Scholar 

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Acknowledgements

This research received specific grant from National High Technology Research Development Plan (2013AA041106), National Natural Science Foundation (62073213), China Postdoctoral Science Foundation (2014M561458), Shanghai Natural Science Foundation of China (23ZR1426700), Shanghai Engineering Technology Research Center Construction projects (20DZ2253300).

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Authors and Affiliations

  1. Logistics Engineering College, Shanghai Maritime University, Shanghai, 201306, China

    Biao Li, Bing Wang, Xiong Hu, Jianhui Zhai & Changping Ji

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  1. Biao Li
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  2. Bing Wang
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  3. Xiong Hu
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  4. Jianhui Zhai
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  5. Changping Ji
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Contributions

BL was involved in writing–original draft, software, methodology. BW was involved in supervision, writing–review & editing, investigation. XH was involved in funding acquisition, validation. JZ was involved in drawing diagrams, data validation; CJ was involved in software, data validation.

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Correspondence to Bing Wang.

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Li, B., Wang, B., Hu, X. et al. A small defect detection technique for industrial product surfaces based on the EA-YOLO model. J Supercomput 81, 415 (2025). https://doi.org/10.1007/s11227-025-06929-0

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