Lightweight and Improved <scp>PCBA</scp> Welding Defect Detection Algorithm of <scp>YOLOv8n</scp>

ABSTRACT Aiming at the problems of large computational complexity and high missed detection rate existing in the current printed circuit board assembly (PCBA) solder joint defect detection algorithms, and considering the requirements of rapid detection and easy deployment in industrial production, a PCBA solder joint defect detection method based on the lightweight improvement of the YOLOv8n model is proposed. Firstly, the backbone network is replaced with the HGNetV2 network. Through the effective extraction of multi‐scale features, the PCBA defect detection effect is improved and the storage requirements and computational resource consumption of the model are reduced. By replacing the Bottleneck module in C2f with the RepViTBlock module, a C2f‐RVB structure is designed, which improves the accuracy while reducing the number of parameters. The lightweight asymmetric detection head replacement further reduces model parameters and improves detection speed. Experimental results showed that the accuracy of the improved YOLOv8n model is increased by 5.9%, the number of parameters is reduced by 53.8%, GFLOPs are reduced by 53.1%, and model size is reduced by 49.2%. The experimental results indicate that the improved model can meet the requirements of real‐time detection of PCBA solder joint defects, and the relatively low number of parameters enables lightweight deployment of the model, which has certain reference value.