Design and Application of Defect Detection System Based Deep Learning
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摘要: 采用人工方法的瑕疵检测需要耗费大量人力,成本较高,且视觉疲劳及人为主观理解差异容易产生判断误差;采用传统方法的瑕疵检测抗干扰性差、参数设置复杂、应用门槛高,且对于复杂背景和缺陷鲁棒性差。针对上述问题,开发一套可便捷、快速地进行模型构建并能够对深度学习算法性能进行评估的集成软件系统。该系统具有标注、训练、检测及复查功能,且集成了端到端瑕疵识别、目标定位及分类功能的目标检测算法,能够实现对瑕疵进行像素级分割和分类。同时,该系统仅需训练正常样本就能进行正常样本和异常样本的分类检测,应用潜力巨大。Abstract: The defect detection using manual method requires a lot of manpower and high cost.Moreover,visual fatigue and differences in human subjective understanding are easy to produce judgment errors.The traditional method of defect detection has poor anti-interference ability,complex parameter setting,high application threshold,and poor robustness to complex background and defects.In order to solve the problem,an integrated software system is developed,which can easily and quickly build models and evaluate the performance of deep learning algorithms.The system has the functions of labeling,training,detection,review,and integrates the target detection algorithm with the functions of end-to-end defect recognition,target location and classification,which can achieve pixel-level segmentation and classification of defects.Meanwhile,the system can classify and detect normal and abnormal samples only by training normal samples,which has great application potential.
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Keywords:
- deep learning /
- defect detection /
- object detection /
- semantic segmentation /
- abnormal detection
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[1] REDMON J, DIVVALA S, GIRSHICK R, et al.You only look once:unified, real-time object detection[C]. Proceedings of the IEEEConference on Computer Vision and Pattern Recognition, 2016.
[2] REDMON J, FARHADI A.YOLO9000:better, faster, stronger[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017.
[3] FARHADI A, REDMON J.Yolov3:an incremental improvement[C]. ComputerVision and Pattern Recognition, 2018.
[4] HU X, LIU Y, ZHAO Z, et al.Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network[J]. Computers and Electronics in Agriculture, 2021(185):106135.
[5] TANG P, RAMAIAH C, WANG Y, et al.Proposal learning for semi-supervised object detection[C]. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2021.
[6] REN S, HE K, GIRSHICK R, et al.Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 39(6):1137-1149.
[7] LECUN Y, BOTTOU L, BENGIO Y, et al.Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.
[8] TOMPSON J, GOROSHIN R, JAIN A, et al.Efficient object localization using convolutional networks[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
[9] GIRSHICK R, DONAHUE J, DARRELL T, et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2014.
[10] BROCK A, DE S, SMITH S L, et al.High-performance large-scale image recognition without normalization[C]. International Conference on Machine Learning, 2021.
[11] SZEGEDY C, LIU W, JIA Y, et al.Going deeper with convolutions[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2015.
[12] HE K M, ZHANG X Y, REN S Q, et al.Deep residual learning for image recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
[13] CARION N, MASSA F, SYNNAEVE G, et al.End-to-end object detection with transformers[C]. European Conference on Computer Vision(ECCV), 2020.
[14] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, etal.Animage is worth 16x16 words:transformers for image recognition at scale[C]. International Conference on Learning Representations(ICLR), 2021.
[15] ZHANG B W, TIAN Z, TANG Q, et al.SegViT:semantic segmentation with plain vision transformers[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[16] HE H, CAI J, PAN Z, et al.Dynamic focus-aware positional queries for semantic segmentation[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
[17] RONNEBERGER O L, FISCHER P, BROX T.U-Net:convolutional networks for biomedical image segmentation[C]. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015.
[18] LI C L, SOHN K, YOON J, et al.Cutpaste:self-supervised learning for anomaly detection and localization[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021.
[19] AHMED S, NIELSEN I E, TRIPATHI A, et al.Transformers in time-series analysis:a tutorial[J]. Circuits, Systems, and Signal Processing, 2023, 42(12):7433-7466.
[20] ROTH K, PEMULA L, ZEPEDA J, et al.Towards total recall in industrial anomaly detection[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.
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