YOLO algorithm with hybrid attention feature pyramid network for solder
  joint defect detection
Li Ang Siti Khatijah Nor Abdul Rahim Raseeda Hamzah Raihah Aminuddin Gao Yousheng
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.