Optimization of automatic PCB defect detection algorithm based on machine learning
基于机器学习的PCB缺陷自动检测算法优化

摘要

随着电子产品的日益复杂化，印刷电路板（PCB）的质量控制变得愈加重要。传统的人工检测方法不仅费时费力，而且容易出现误差。基于机器学习的PCB缺陷自动检测算法逐渐成为解决这一问题的有效途径。本文针对PCB缺陷检测中的难点，提出了一种基于深度学习的自动化检测算法。通过对PCB图像数据的深度特征提取和分类模型优化，显著提高了检测的准确性和效率。实验结果表明，该算法在实际应用中具有较强的鲁棒性，并能够有效减少人工干预。本文的研究不仅为PCB的自动检测提供了一种可行方案，也为其他工业自动化检测问题提供了借鉴意义。

Abstract

With the increasing complexity of electronic products, the quality control of printed circuit boards (PCBs) has become more and more important. Traditional manual detection methods are not only time-consuming and labor-intensive, but also prone to errors. The automatic PCB defect detection algorithm based on machine learning has gradually become an effective way to solve this problem. Aiming at the difficulties in PCB defect detection, this paper proposes an automated detection algorithm based on deep learning. By extracting the deep features of PCB image data and optimizing the classification model, the accuracy and efficiency of detection are significantly improved. The experimental results show that the algorithm has strong robustness in practical applications and can effectively reduce manual intervention. The research of this paper not only provides a feasible solution for the automatic detection of PCBs, but also provides reference for other industrial automated detection problems.