Design of automatic detection and fault localization system for PCB circuit boards based on machine vision
基于机器视觉的PCB电路板自动检测与故障定位系统设计

摘要

PCB电路板的质量直接关系到电子产品的性能与可靠性，传统人工检测方式存在效率低、准确性差等问题。本文以机器视觉技术为核心，构建了一种PCB电路板自动检测与故障定位系统，旨在实现高速、高精度的检测。系统通过图像采集、预处理、特征提取与模式识别等步骤，实现焊点缺陷、断路、短路等常见问题的快速识别与定位。研究重点在于结合深度学习算法提升检测精度，并利用图像匹配与模板对比方法提高定位的鲁棒性。实验结果表明，该系统在处理复杂电路板结构时依然保持较高检测准确率，具有较强的工程应用前景。研究为电子制造行业智能化发展提供了可行路径，并在降低人力成本与提升产品质量方面展现出显著优势。

Abstract

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.