High-Precision Detection Algorithm for Metal Workpiece Defects Based on Deep Learning
Author:
Xu Xiujin1, Zhang Gengming1, Zheng Wenhe2, Zhao Anbang1, Zhong Yi1, Wang Hongjun13
Affiliation:
1. College of Engineering, South China Agricultural University, Guangzhou 510642, China 2. Wanhui Hardware Shenzhen Co., Ltd., Shenzhen 518118, China 3. Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China
Abstract
Computer vision technology is increasingly being widely applied in automated industrial production. However, the accuracy of workpiece detection is the bottleneck in the field of computer vision detection technology. Herein, a new object detection and classification deep learning algorithm called CSW-Yolov7 is proposed based on the improvement of the Yolov7 deep learning network. Firstly, the CotNet Transformer structure was combined to guide the learning of dynamic attention matrices and enhance visual representation capabilities. Afterwards, the parameter-free attention mechanism SimAM was introduced, effectively enhancing the detection accuracy without increasing computational complexity. Finally, using WIoUv3 as the loss function effectively mitigated many negative influences during training, thereby improving the model’s accuracy faster. The experimental results manifested that the mAP@0.5 of CSW-Yolov7 reached 93.3%, outperforming other models. Further, this study also designed a polyhedral metal workpiece detection system. A large number of experiments were conducted in this system to verify the effectiveness and robustness of the proposed algorithm.
Funder
Laboratory of Lingnan Modern Agriculture Project Guangzhou Science and Technology Project
Subject
Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Optimization,Mechanical Engineering,Computer Science (miscellaneous),Control and Systems Engineering
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