YOLO Algorithm With Hybrid Attention Feature Pyramid Network for Solder Joint Defect Detection
Ang Li, Raseeda Hamzah, Siti Khatijah Nor Abdul Rahim, Gao Yousheng
Abstract
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 (HAM) 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 HAM comprises a proposed enhanced multihead self-attention and coordinate attention (CA) mechanisms to increase the ability of attention networks to perceive contextual information and enhance the utilization range of network features. The CA mechanism enhances the connection between different channels and reduces location information loss. The HAM 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 mean average precision (mAP) reaching 91.5% and 4.3% higher than the You Only Look Once version 5 (YOLOv5) algorithm and better than other comparative algorithms. Compared to other versions, mAP, precision, recall, F1-score, and frame per seconds indicators have also improved. The improvement of detection accuracy can be achieved while meeting real-time detection requirements.