Lightweight Surface Defect Detection Method of Metal Products Based on YOLOv5s

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Lightweight Surface Defect Detection Method of Metal Products Based on YOLOv5s

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[1]JIA Yunfei,ZHENG Hongmu,LIU Shanliang.Lightweight Surface Defect Detection Method of Metal Products Based on YOLOv5s[J].Journal of Zhengzhou University (Engineering Science),2022,43(05):31-38.[doi:10.13705/j.issn.1671-6833.2022.05.001]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43卷 Number of periods: 2022 05 Page number: 31-38 Column: Public date: 2022-08-22

Title:: Lightweight Surface Defect Detection Method of Metal Products Based on YOLOv5s

Author(s):: JIA Yunfei; ZHENG Hongmu; LIU Shanliang; School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China

Keywords:: surface defect; object detection; light weight; YOLOv5s; tensorRT

CLC:: TP391. 7

DOI:: 10.13705/j.issn.1671-6833.2022.05.001

Abstract:: In order to reduce the intelligent cost in the enterprise, the hardware equipment with low cost and low computing power was used to detect the defects of products through the object detection algorithm model in deep learning. Based on the YOLOv5s network in target detection, this study adopts the idea of structure cutting, sparsely training the network based on the BN layer, and cuting the sparsely trained model corresponding to the layer with small weight value, so as to reduce the number of calculation parameters and the size of model file and to achieve the effect of lightweight. Finally, the trained pruning model was hierarchically fused using NVIDIA′s accelerated framework TensorRT to realize the reasoning acceleration effect. The experimental results showed that the weight file size of this model was reduced by about 70% compared with the original YOLOv5s model, and the detection accuracy on the public dataset NEU-DET reached 74.2%. In the high-performance experimental platform built in this study, the single graph inference speed was improved by 11.3% compared with the original model, and the network had no accuracy loss. In the low-performance experimental platform compared with the original network model, the inference speed of this model increased by 165%, which was more significantly improved than the results in the high-performance experimental platform, indicating that this model perform well in low computing power hardware devices. Then the model was tested by using the open top view data set of submersible pump impeller. At last, the inference acceleration framework TensorRT is used to accelerate the model in this study, and the inference time of single figure 5.8 ms can be achieved on the high-performance experimental platform. The experimental results showed that the inference speed of this model could be greatly improved on low computing power hardware equipment, which could help enterprises reduce their budget.

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Last Update: 2022-08-20