| 基于YOLOv4算法的冲压件缺陷检测 |
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| 引用本文： | 孙永鹏,钟佩思,刘梅,曹爱霞,李梁. 基于YOLOv4算法的冲压件缺陷检测[J]. 锻压技术, 2022, 47(1): 222-228. DOI: 10.13330/j.issn.1000-3940.2022.01.032 |
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| 作者姓名： | 孙永鹏 钟佩思 刘梅 曹爱霞 李梁 |
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| 作者单位： | 山东科技大学 先进制造技术研究中心, 山东 青岛266590;山东科技大学 机械电子工程学院, 山东 青岛266590;山东科技大学 机械电子工程学院, 山东 青岛266590;青岛黄海学院 智能制造学院, 山东 青岛266427 |
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| 基金项目： | 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)； |
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| 摘 要： | 针对冲压件缺陷检测目前存在的人工检测强度大、效率低等问题,提出了一种基于改进YOLOv4(You Only Look Once)模型的快速检测算法(YOLOv4-Mobile).该方法使用改进的MobileNetV3网络代替YOLOv4结构中的CSPDarknet53网络,改进的MobileNetV3网络结合了深度可分...
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| 关 键 词： | 冲压件 缺陷检测 YOLOv4 K-means MobileNetV3 |
Defect detection of stamping parts based on YOLOv4 algorithm |
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| Sun Yongpeng,Zhong Peisi,Liu Mei,Cao Aixia,Li Liang. Defect detection of stamping parts based on YOLOv4 algorithm[J]. Forging & Stamping Technology, 2022, 47(1): 222-228. DOI: 10.13330/j.issn.1000-3940.2022.01.032 |
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| Authors: | Sun Yongpeng Zhong Peisi Liu Mei Cao Aixia Li Liang |
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| Affiliation: | (Advanced Manufacturing Technology Center,Shandong University of Science and Technology,Qingdao 266590,China;College of Mechanical and Electronic Engineering,Shandong University of Science and Technology,Qingdao 266590,China;College of Intelligent Manufacturing,Qingdao Huanghai University,Qingdao 266427,China) |
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| 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. |
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| Keywords: | stamping parts defect detection YOLOv4 K-means MobileNetV3 |
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