PCB-YOLO: An Improved Detection Algorithm of PCB Surface Defects Based on YOLOv5

Junlong Tang (), Shenbo Liu, Dongxue Zhao, Lijun Tang, Wanghui Zou and Bin Zheng
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Junlong Tang: School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha 410114, China
Shenbo Liu: School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha 410114, China
Dongxue Zhao: School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha 410114, China
Lijun Tang: School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha 410114, China
Wanghui Zou: School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha 410114, China
Bin Zheng: School of Computer and Communications Engineering, Changsha University of Science and Technology, Changsha 410114, China

Sustainability, 2023, vol. 15, issue 7, 1-17

Abstract: To address the problems of low network accuracy, slow speed, and a large number of model parameters in printed circuit board (PCB) defect detection, an improved detection algorithm of PCB surface defects based on YOLOv5 is proposed, named PCB-YOLO, in this paper. Based on the K-means++ algorithm, more suitable anchors for the dataset are obtained, and a small target detection layer is added to make the PCB-YOLO pay attention to more small target information. Swin transformer is embedded into the backbone network, and a united attention mechanism is constructed to reduce the interference between the background and defects in the image, and the analysis ability of the network is improved. Model volume compression is achieved by introducing depth-wise separable convolution. The EIoU loss function is used to optimize the regression process of the prediction frame and detection frame, which enhances the localization ability of small targets. The experimental results show that PCB-YOLO achieves a satisfactory balance between performance and consumption, reaching 95.97% mAP at 92.5 FPS, which is more accurate and faster than many other algorithms for real-time and high-precision detection of product surface defects.

Keywords: PCB defect detection; YOLO; united attention mechanism; PCB-YOLO (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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