Abstract
Addressing the issues of large model size and slow detection speed in real-time defect detection in complex scenarios of printed circuit boards (PCBs), this study proposes a new lightweight defect detection model called SCF-YOLO. The aim of SCF-YOLO is to solve the problem of resource limitation in algorithm deployment. SCF-YOLO utilizes the more compact and lightweight MobileNet as the feature extraction network, which effectively reduces the number of model parameters and significantly improves the inference speed. Additionally, the model introduces a learnable weighted feature fusion module in the neck, which enhances the expression of features at multiple scales and different levels, thus improving the focus on key features. Furthermore, a novel SCF module (Synthesis C2f) is proposed to enhance the model's ability to capture high-level semantic features. During the training process, a combined loss function that combines CIoU and GIoU is used to effectively balance the optimization of different objectives and ensure the precise location of defects. Experimental results demonstrate that compared to the YOLOv8 algorithm, SCF-YOLO reduces the number of parameters by 25% and improves the detection speed by up to 60%. This provides a fast, accurate, and efficient solution for defect detection of PCBs in industrial production.