A lightweight YOLOv8 for small target defect detection on printed circuit boards

Abstract

This study proposes a lightweight YOLOv8 detection model to tackle the performance and efficiency challenges in detecting small targets on printed circuit boards (PCBs) with surface defects. Firstly, the CA (Coordinate Attention) mechanism is integrated into the core of the model to enhance its capability in processing PCB image features, thus boosting the model's robustness and generalization. Secondly, DepSepConv (Depthwise Separable Convolution) is employed in the neck part to replace the original C2f module, significantly reducing computational requirements and parameter count, while improving detection performance across various defective targets. Additionally, an enhanced Efficient_Detect network structure is introduced to further trim model parameters, computational complexity, and enhance resource utilization. Through experimental validation, the proposed method achieves a 96.5% mAP50, outperforming the original YOLOv8n model by 2.4%. The model's parameters, GFLOPs, and weight sizes are reduced by 39.4%, 44.4%, and 39.3%, respectively, with current parameters standing at 1.74 M, GFLOPs at 4.5, and weight sizes at 3.7 M. Furthermore, the detection speed reaches 141 FPS, indicating its potential for real-time detection. This research presents an effective solution for scenarios with limited computational resources and holds promise for application in the field of circuit board defect detection.