Transactions of Materials and Heat Treatment

Title：Defect detection of stamping parts based on YOLOv4 algorithm

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ClassificationCode：TP391.41；TG38

year,vol(issue):pagenumber：2022,47(1):222-228

Abstract：

For the problems of high manual detection intensity and low efficiency in defect detection of stamping parts at present, a fast detection algorithm (YOLOv4-Mobile) based on the improved YOLOv4 (You Only Look Once) model was proposed, which used the improved MobileNetV3 network to replace the CSPDarknet53 network in YOLOv4 structure, and the improved MobileNetV3 network combined a depthwise separable convolution, an inverted residual structure with a linear bottleneck and SE (Squeeze and Excitation) structure. Then, the image of stamping parts collected in the workshop was used to establish the defect data set and enhance the data set, and a set of prior frame parameters corresponding to the defect data set of stamping parts was obtained by K-means clustering algorithm to improve the matching degree of prior frame and feature layer. The test results show that based on the improved YOLOv4 model, the mAP(mean Average Precision) of the fast detection algorithm reaches 89%, which is higher than that of SSD algorithm. Meanwhile, the detection speed reaches 0.15 s per sheet, which is better than the original YOLOv4 algorithm.

Funds：

山东省重点研发计划资助项目(2019GGX104102)；山东省自然科学基金资助项目(ZR2017MEE066)

作者简介：孙永鹏(1995-)，男，硕士研究生 E-mail：sunyongpenglvlife@163.com 通信作者：钟佩思(1966-)，男，博士，教授 E-mail：pszhong@163.com

Reference：

[1] 邓凡,刘彦强,樊建中,等.基于数字图像相关技术的泡沫铝复合结构的弯曲行为研究[J].稀有金属,2021,45(3):297-305.

Deng F, Liu Y Q, Fan J Z, et al. Observation of bending behavior of aluminum foam composite structure based on digital image correlation technology[J]. Chinese Journal of Rare Metals, 2021,45(3):297-305.

[2] 陶显,侯伟,徐德.基于深度学习的表面缺陷检测方法综述[J/OL].自动化学报:1-19[2020-10-11]. http://kns.cnki.net/kcms/detail/11.2109.TP.20200402.1101.002.html.

Tao X, Hou W, Xu D. A survey of surface defectdetection methods based on deep learning[J/OL]. Acta Automatica Sinica: 1-19[2020-10-11]. http://kns.cnki.net/kcms/detail/11.2109. TP.20200402.1101.002.html.

[3] 李兰,奚舒舒,张才宝,等.基于改进SSD模型的工件表面缺陷识别算法[J].计算机工程与科学,2020,42(9):1608-1615.

Li L, Xi S S, Zhang C B, et al. A surface defect recognition algorithm based on improver SSD model[J]. Computer Engineering and Science, 2020, 42(9): 1608-1615.

[4] 袁野,谭晓阳.复杂环境下的冰箱金属表面缺陷检测[J/OL].计算机应用:1-6[2020-09-22].

Yuan Y, Tan X Y. Defect detection of refrigerator metal surface in complex environment[J].Journal of Computer Applications: 1-6[2020-09-22].

[5] 李春霖,谢刚,王银,等.基于YOLOv3-TinyD算法的偏光片缺陷检测[J/OL].计算机集成制造系统:1-17[2020-09-23].

Li C L, Xie G, Wang Y, et al. Defect detection of polaroid based on YOLOv3-TinyD[J/OL]. Computer Interated Manufacturing Systems: 1-17[2020-09-23].

[6] Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[A]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C]. Washington D. C. : IEEE Press, 2016.

[7] Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: Optimal speed and accuracy of object detection[EB/OL]. https://github.com/AlexeyAB/darknet, 2020.

[8] He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence,2015，37（9）：1904-1916.

[9] Liu S, Qi L, Qin H, et al. Path aggregation network for instance segmentation[A]. IEEE/CVF Conference on Computer Vision and Pattern Recognition[C]. Washington D. C. : IEEE Press, 2018.

[10]Howard A, Sandler M, Chen B, et al. Searching for MobileNetV3[A]. 2019 IEEE/CVF International Conference on Computer Vision (ICCV)[C]. Seoul: IEEE, 2020.

[11]Elsken T, Metzen J H, Hutter F. Neural architecture search: A survey[J].Journal of Machine Learning Research,2019,20:1-21.

[12]Howard A G, Zhu M, Chen B, et al. MobileNets: Efficient convolutional neural networks for mobile vision applications[A]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C]. Hawaii: IEEE, 2017.

[13]Sandler M, Howard A, Zhu M, et al. MobileNetV2: Inverted residuals and linear bottlenecks[A]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C]. Salt Lake City: IEEE, 2018.

[14]Hu J, Shen L, Sun G, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 42(8):7132-7141.

[15]Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multi box detector[A]. Proc of European Conference on Computer Vision[C]. Berlin: Springer, 2016.

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