Tang Ying, Liu Runhao, Wang Sheng
School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China.
School of Computer and Network Security, Chengdu University of Technology, Chengdu 610059, China.
Micromachines (Basel). 2025 Apr 27;16(5):509. doi: 10.3390/mi16050509.
Aiming at the demand for defect detection accuracy and efficiency under the trend of high-density and integration in printed circuit board (PCB) manufacturing, this paper proposes an improved YOLOv8n model (YOLO-SUMAS), which enhances detection performance through multi-module collaborative optimization. The model introduces the SCSA attention mechanism, which improves the feature expression capability through spatial and channel synergistic attention; adopts the Unified-IoU loss function, combined with the dynamic bounding box scaling and bi-directional weight allocation strategy, to optimize the accuracy of high-quality target localization; integrates the MobileNetV4 lightweight architecture and its MobileMQA attention module, which reduces the computational complexity and improves the inference speed; and combines ASF-SDI Neck structure with weighted bi-directional feature pyramid and multi-level semantic detail fusion to strengthen small target detection capability. The experiments are based on public datasets, and the results show that the improved model achieves 98.8% precision and 99.2% recall, and mAP@50 reached 99.1%, significantly better than the original YOLOv8n and other mainstream models. YOLO-SUMAS provides a highly efficient industrial-grade PCB defect detection solution by considering high precision and real-time performance while maintaining lightweight characteristics.
针对印刷电路板（PCB）制造中高密度和集成化趋势下对缺陷检测精度和效率的需求，本文提出了一种改进的YOLOv8n模型（YOLO-SUMAS），通过多模块协同优化来提升检测性能。该模型引入了SCSA注意力机制，通过空间和通道协同注意力提高特征表达能力；采用统一IoU损失函数，结合动态边界框缩放和双向权重分配策略，优化高质量目标定位的精度；集成了MobileNetV4轻量级架构及其MobileMQA注意力模块，降低计算复杂度并提高推理速度；并将ASF-SDI Neck结构与加权双向特征金字塔和多级语义细节融合相结合，增强小目标检测能力。实验基于公开数据集，结果表明改进后的模型精度达到98.8%，召回率达到99.2%，mAP@50达到99.1%，显著优于原始的YOLOv8n和其他主流模型。YOLO-SUMAS在保持轻量级特性的同时兼顾高精度和实时性能，提供了一种高效的工业级PCB缺陷检测解决方案。
