基于SDA-YOLOv8的PCBA缺陷检测算法研究OA

To address the challenges of small defect targets,high computational load,and excessively large generative model files in the defect detection of surface-mounted component on printed circuit boards,this paper proposes a lightweight defect detection method based on an improved YOLOv8.By adop-ting the StarNet architecture,efficient feature fusion is achieved with reduced computational cost.A detail-enhanced lightweight detection head is designed to transmit more low-level feature information to the high-dimensional detection network,thereby improving the detection performance for small target de-fects.Additionally,the original SPPF module is replaced with the PRP-AIFI module,enabling more effec-tive capture of key information points.Experimental results on a custom dataset demonstrate that the im-proved model achieves a precision of 99.3%,with the mean Average Precision(mAP)increasing by 1.3%compared to the original model.The number of model parameters is reduced by 40.9%,and the FLOPs are reduced to 5.0×109,representing a 39%decrease relative to the baseline.The proposed algorithm signifi-cantly enhances computational efficiency while maintaining high accuracy.