Research on improvement of defect detection system for printed circuit board based on deep learning

Abstract

As the core component of electronic products, the quality of PCB (print circuit board) directly affects the performance and reliability of the entire product. The traditional PCB defect detection methods mainly rely on manual visual inspection and image processing technology, which have problems such as low efficiency and easy errors and cannot meet the needs of large-scale production. The PCB defect detection method based on deep learning and object detection technology has gradually become mainstream. This article focuses on the research of PCB defect object detection method based on deep learning. Therefore, the paper conducts relevant intensive study on PCB defect detection, aiming to develop a model that is accurate, compact, and suitable for real-time detection. Specific improvements include using C2F_The ViT module replaces the original C2F, and the purpose is to facilitate the model to comprehensively consider the local details of PCB defects and the global contextual semantic information. Introduces multi-head self-attention mechanism (MHSA), adds detection heads in the p2 layer, and improves defect detection accuracy and location accuracy. Extract the target semantic information of small defects and suppress the local background information at the same time. The experimental results indicate that the detection algorithm can improve detection accuracy. Compared with the original YOLOv8, the improved model has a 4.9% and 7.9% improvement in map50 and map50-95, respectively.