Advancements in Electric Vehicle PCB Inspection: Application of Multi-Scale CBAM, Partial Convolution, and NWD Loss in YOLOv5

Abstract

In the rapidly evolving electric vehicle industry, the reliability of electronic systems is critical to ensuring vehicle safety and performance. Printed circuit boards (PCBs), serving as a cornerstone in these systems, necessitate efficient and accurate surface defect detection. Traditional PCB surface defect detection methods, like basic image processing and manual inspection, are inefficient and error-prone, especially for complex, minute, or irregular defects. Addressing this issue, this study introduces a technology based on the YOLOv5 network structure. By integrating the Convolutional Block Attention Module (CBAM), the model’s capability in recognizing intricate and small defects is enhanced. Further, partial convolution (PConv) replaces traditional convolution for more effective spatial feature extraction and reduced redundant computation. In the network’s final stage, multi-scale defect detection is implemented. Additionally, the normalized Wasserstein distance (NWD) loss function is introduced, considering relationships between different categories, thereby effectively solving class imbalance and multi-scale defect detection issues. Training and validation on a public PCB dataset showed the model’s superior detection accuracy and reduced false detection rate compared to traditional methods. Real-time monitoring results confirm the model’s ability to accurately detect various types and sizes of PCB surface defects, satisfying the real-time detection needs of electric vehicle production lines and providing crucial technical support for electric vehicle reliability.