Traditional manual detection for solder joint defect is no longer applied during industrial production due to low efficiency, inconsistent evaluation, high cost and lack of real-time data. A new approach has been proposed to address the issues of low accuracy, high false detection rates and computational cost of solder joint defect detection in surface mount technology of industrial scenarios. The proposed solution is a hybrid attention mechanism designed specifically for the solder joint defect detection algorithm to improve quality control in the manufacturing process by increasing the accuracy while reducing the computational cost. The hybrid attention mechanism comprises a proposed enhanced multi-head self-attention and coordinate attention mechanisms increase the ability of attention networks to perceive contextual information and enhances the utilization range of network features. The coordinate attention mechanism enhances the connection between different channels and reduces location information loss. The hybrid attention mechanism enhances the capability of the network to perceive long-distance position information and learn local features. The improved algorithm model has good detection ability for solder joint defect detection, with mAP reaching 91.5%, 4.3% higher than the You Only Look Once version 5 algorithm and better than other comparative algorithms. Compared to other versions, mean Average Precision, Precision, Recall, and Frame per Seconds indicators have also improved. The improvement of detection accuracy can be achieved while meeting real-time detection requirements.

翻译：传统人工焊点缺陷检测因效率低、标准不一、成本高且缺乏实时数据，已不再适用于工业生产。针对工业场景表面贴装技术中焊点缺陷检测存在精度低、误检率高及计算成本高的问题，提出了一种新方法。该方案是一种专为焊点缺陷检测算法设计的混合注意力机制，通过提高精度同时降低计算成本来改善制造过程中的质量控制。该混合注意力机制包含所提出的增强型多头自注意力与坐标注意力机制，增强了注意力网络感知上下文信息的能力，并扩大了网络特征的利用范围。坐标注意力机制增强了不同通道间的联系，减少了位置信息损失。混合注意力机制提升了网络感知长距离位置信息和学习局部特征的能力。改进后的算法模型对焊点缺陷检测具有良好的检测能力，平均精度均值达到91.5%，比You Only Look Once版本5算法高4.3%，且优于其他对比算法。与其他版本相比，平均精度均值、精确率、召回率和每秒帧数等指标均有提升，可在满足实时检测要求的同时提高检测精度。