Abstract
Due to the tiny size and the fuzzy pixels of tiny objects, tiny defect detection is a thorny problem in industry application. Meanwhile, the real-time detection of tiny defects is highly required in industrial assembly line. That is to say, tiny defect detection algorithms must ensure high accuracy while maintaining a low computational cost. This paper designs a lightweight and efficient network for tiny defect detection. In the backbone network, Diagonal Feature Pyramid (DFP) is proposed to improve the performance of tiny defect detection. For higher accuracy, DFP fuses more original features if they are at the same level. For less computational cost, DFP reduces the model size by eliminating the bottom-up pathway and removing some non-original same-level features. In the neck network, a multi-scale neck network with some fusion strategies is designed to suit multi-scale tiny defect detection. Finally, an adaptive localization loss function is designed to adjust the sensitivity of tiny defects. Based on a public PCB (Printed Circuit Board) dataset, the comparative experiments show that our model has better mAP and higher speed than various mainstream defect detection algorithms.
Similar content being viewed by others
References
Lin TY, Dollar P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2117–2125
Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 8759–8768. 1, 2, 7
Tan M, Pang R, Le QV (2020) EfficientDet: scalable and efficient object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2:779–788
Viola P, Jones MJ (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–I
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
Felzenszwalb PF, McAllester D, Ramanan D (2008) A discriminatively trained, multiscale, deformable part model. 2008 IEEE Conference on Computer Vision and Pattern Recognition, pages 1-8
Raihan FI, Ce W (2017) PCB defect detection USING OPENCV with image subtraction method. 2017 International Conference on Information Management and Technology (ICIMTech), pages. 204–209
Dong N, Wu C-H, Ip A, Chen Z, Yung K (2012) Chaotic species based particle swarm optimization algorithms and its application in PCB components detection. Expert Syst Appl 39:12501–12511
Lu Z, He Q, Xiang X, Liu H (2018) Defect detection of PCB based on Bayes feature fusion. The Journal of Engineering 2018:1741–1745
Girshick RB, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp 580-587
Girshick RB (2015) "Fast R-CNN," 2015 IEEE International Conference on Computer Vision (ICCV), pages. 1440–1448
Ren S, He K, Girshick RB, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 39:1137–1149
Dai J, Li Y, He K, Sun J (2016) R-FCN:Object detection via region-based fully convolutional networks. In Advances in Neural Information Processing Systems (NIPS), pages 379–387
Pang J, Chen K, Shi J, Feng H, Ouyang W, Lin D (2019) Libra R-CNN: Towards balanced learning for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 821–830
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) SSD: Single shot multibox detector. In Proceedings of the European Conference on Computer Vision (ECCV), pages 21–37
Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 7263–7271
Redmon J, Farhadi A (2018) YOLOv3: An Incremental Improvement. ArXiv, vol. abs/1804.02767
Bochkovskiy A, Wang C-Y, Liao H (2020) YOLOv4: optimal speed and accuracy of object detection, ArXiv, vol. abs/2004.10934
Lin T-Y, Goyal P, Girshick R, He K, Dollar P (2017) Focal loss for dense object detection.In ´Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 2980–2988
He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 37(9):1904–1916
Ghiasi G, Lin T-Y, Le QV (2019) NAS-FPN: Learning scalable feature pyramid architecture for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 7036–7045
Hu B, Wang J (2020) Detection of PCB surface defects with improved faster-RCNN and feature pyramid network[J]. IEEE Access 8:108335–108345
Wu J, Le J, Xiao Z et al (2021) Automatic fabric defect detection using a wide-and-light network. Appl Intell 51:4945–4961. https://doi.org/10.1007/s10489-020-02084-6
Li D, Fu SL, Zhang QJ, Mo Y, Liu L, Xu CF (2020) An improved PCB defect detector based on feature pyramid networks. 2020 International Conference on Computer Science and Artificial Intelligence (CSAI). pages. 233–239
Ding R, Dai L, Li G, Liu H (2019) TDD-net: a tiny defect detection network for printed circuit boards. CAAI Trans Intell Technol 4:110–116
Li Z, Peng C, Yu G, Zhang X, Deng Y, Sun J (2018) DetNet: design backbone for object detection. In Proceedings of the European Conference on Computer Vision (ECCV), pages 334–350
Li J, Liang X, Wei Y, Xu T, Feng J, Yan S (2017) Perceptual generative adversarial networks for small object detection, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages. 1951–1959
Chen Y, Zhang H, Liu L, Chen X, Zhang Q, Yang K, Xia R, Xie J (2021) Research on image Inpainting algorithm of improved GAN based on two-discriminations networks. Appl Intell 51:3460–3474. https://doi.org/10.1007/s10489-020-01971-2
Huang g, Liu z, van der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pages. 4700–4708
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yu, Z., Wu, Y., Wei, B. et al. A lightweight and efficient model for surface tiny defect detection. Appl Intell 53, 6344–6353 (2023). https://doi.org/10.1007/s10489-022-03633-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-022-03633-x