基于改进YOLOv4算法的轮毂表面缺陷检测

doi:10.3969/j.issn.1000-1158.2022.11.03

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Abstract The surface defects produced during the processing of automobile wheels seriously affect the aesthetics and service performance of the whole vehicle. Aiming at the problems of low manual defect detection efficiency and high missed detection rate at this stage, a method for detecting wheel surface defects based on the improved YOLOv4 algorithm is proposed. Constructed a wheel defect data set, including six types of surface defects, composed of 2346 images of 4928×3264pixel, using K-means method for priori box clustering, and focusing on YOLOv4 algorithm on small-scale defects such as fiber and sticky aluminum insufficient detection accuracy. In the Neck part of the original network, a thinned U-shaped network module (TUM) and attention mechanism are introduced to enhance effective features and suppress invalid features, strengthen multi-scale feature extraction and fusion, and improve the possibility of feature processing small target information loss problem; based on self-built data sets, training and testing the defect detection performance of different algorithms and verify the effectiveness of the improved modules, the results show that the average defect detection accuracy of the method reaches 85.8%, and it greatly improves small-size defects such as aluminum sticking. The detection ability of, the detection accuracy is the highest among many comparison algorithms.

Key words： metrology wheel hub defect detection improved YOLOv4 algorithm TUM

Received: 19 July 2021 Published: 14 November 2022

PACS:

TP92

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	Articles by authors
	WU Feng-he
	CUI Jian-xin
	ZHANG Ning
	ZHANG Zhi-liang
	ZHANG Hui-long
	GUO Bao-su

Cite this article:

WU Feng-he,CUI Jian-xin,ZHANG Ning, et al. Surface Defect Detection of Wheel Hub Based on Improved YOLOv4 Algorithm[J]. Acta Metrologica Sinica, 2022, 43(11): 1404-1411.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2022.11.03 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2022/V43/I11/1404

［1］郭保苏, 吴文文, 付强, 等. 基于支持向量机分类策略的多晶硅电池片色差检测［J］. 计量学报, 2019, 40(6):1013-1019.
Guo B S, Wu W W, Fu Q, et al. Color Difference Detection of Polycrystalline Silicon Cells Based on Support Vector Machine Classification Strategy［J］. Acta Metrologica Sinica, 2019, 40(6):1013-1019.
［2］Cheng J C, Wang M. Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques［J］. Automation in Construction, 2018, 95:155-171.
［3］陈佳倩,金晅宏, 王文远, 等. 基于YOLOv3和DeepSort的车流量检测［J］. 计量学报, 2021, 42(6):718-723.
CHEN J Q, JIN X H, WANG W Y, et al. Vehicle Flow Detection Based on YOLOv3 and DeepSort［J］. Acta Metrologica Sinica, 2021, 42(6):718-723.
［4］陶显, 侯伟, 徐德. 基于深度学习的表面缺陷检测方法综述［J］. 自动化学报, 2020, 47(5):1-18.
Tao X, Hou W, Xu D. A Survey of Surface Defect Detection Methods Based on Deep Learning［J］. Acta Automatica Sinica, 2020, 47(5):1-18.
［5］Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation［C］//Proceedings of the IEEE conference on computer vision and pattern recognition (CVRP). Columbus, USA, 2014:580-587.
［6］Girshick R. Fast RCNN［C］//Proceedings of the IEEE International Conference on Computer Vision (ICCV). Santiago, Chile, 2015:1440-1448.
［7］Ren S Q, He K M, Girshick R, et al. Faster RCNN:to-wards real-time object detection with region proposal net-works［C］//Advances in Neural Information Processing Systems (NIPS). Montreal, Quebec, Canada, 2015:91-99.
［8］Cha Y J, Choi W, Suh G, et al. Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types［J］. Computer Aided Civil and Infrastructure Engineering, 2018, 33(9):731-747.
［9］Liu W, Anguelov D, Erhan D, et al. Ssd:Single shot multibox detector［C］//Proceedings of European Conference on Computer Vision (ECCV). Amsterdam, Netherlands, 2016:21-37.
［10］Chen J, Liu Z, Wang H, et al. Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network［J］. IEEE Transactions on Instrumentation and Measurement, 2017, 67(2):257-269.
［11］Redmon J, Divvala S, Girshick R, et al. You only look once:Unied, real-time object detection［C］//Proceedings of the IEEE conference on computer vision and pattern recognition (CVRP). Las Vegas, USA, 2016. 779-788.
［12］Redmon J, Farhadi A. YOLO9000:Better, faster, stronger［C］//Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVRP). Honolulu, USA, 2017:6517-6525.
［13］Redmon J, Farhadi A. YOLOv3:An incremental improvement［C］// IEEE conference on Computer Vision and Pattern Recognition (CVRP), Salt Lake City, USA, 2018:1804.0276.
［14］翁智, 程曦, 郑志强. 基于改进YOLOv3的高压输电线路关键部件检测方法［J］. 计算机应用, 2020, 40(S2):183-187.
Weng Z, Cheng X, Zheng Z Q. Detection method for key components of high-voltage transmission lines based on improved YOLOv3［J］. Computer application, 2020, 40 (S2):183-187.
［14］谭芳,穆平安,马忠雪. 基于YOLOv3检测和特征点匹配的多目标跟踪算法［J］. 计量学报, 2021, 42(2):157-162.
Tan F, Mu P A, Ma Z X. Multi-target Tracking Algorithm Based on YOLOv3 Detection and Feature Point Matching［J］. Acta Metrologica Sinica, 2021, 42(2):157-162.
［15］Bochkovskiy A, Wang C, Liao H M. Yolov4:Optimal Speed and Accuracy of Object Detection［C］// Proceedings of European Conference on Computer Vision (ECCV). Glasgow, US, 2020:2004.10934.
［16］Xin H, Chen Z, Wang B. PCB Electronic Component Defect Detection Method based on Improved YOLOv4 Algorithm［J］. Journal of physics. Conference series. 2021, 1827(1):12167.