Journal of Electrical Engineering and Automation

The quality of PCB (Printed Circuit Board) directly affects the performance and reliability of electronic products. Traditional manual detection methods suffer from problems such as low efficiency and poor accuracy. Taking machine vision technology as the core, this paper constructs an automatic detection and fault localization system for PCB circuit boards, aiming to achieve high-speed and high-precision detection. Through steps including image acquisition, preprocessing, feature extraction, and pattern recognition, the system realizes rapid identification and localization of common issues such as solder joint defects, open circuits, and short circuits. The research focuses on improving detection accuracy by integrating deep learning algorithms and enhancing the robustness of localization using image matching and template comparison methods. Experimental results show that the system still maintains high detection accuracy when dealing with complex circuit board structures, demonstrating strong prospects for engineering application. This research provides a feasible path for the intelligent development of the electronic manufacturing industry and exhibits significant advantages in reducing labor costs and improving product quality.

Machine vision; PCB detection; Fault localization; Deep learning