Li Yazhou, Wang Yuanyuan, Liu Jiange, Wu Kexiao, Abdullahi Hauwa Suieiman, Lv Pinrong, Zhang Haiyan
College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China.
Huai'an Power Supply Branch, State Grid Jiangsu Electric Power Co., Ltd., Huaian, China.
PLoS One. 2025 Apr 7;20(4):e0318033. doi: 10.1371/journal.pone.0318033. eCollection 2025.
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.
针对印刷电路板（PCB）复杂场景下实时缺陷检测中存在的模型规模大、检测速度慢等问题，本研究提出了一种名为SCF-YOLO的新型轻量级缺陷检测模型。SCF-YOLO的目的是解决算法部署中的资源限制问题。SCF-YOLO利用更紧凑、轻量级的MobileNet作为特征提取网络，有效减少了模型参数数量，并显著提高了推理速度。此外，该模型在颈部引入了一个可学习的加权特征融合模块，增强了多尺度和不同层次特征的表达，从而提高了对关键特征的关注。此外，还提出了一种新颖的SCF模块（合成C2f）来增强模型捕捉高级语义特征的能力。在训练过程中，使用结合CIoU和GIoU的组合损失函数来有效平衡不同目标的优化，并确保缺陷的精确定位。实验结果表明，与YOLOv8算法相比，SCF-YOLO的参数数量减少了25%，检测速度提高了60%。这为工业生产中PCB的缺陷检测提供了一种快速、准确和高效的解决方案。
