Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To ...Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.展开更多
文摘针对不同底纹背景颜色的印制电路板(printed circuit board,PCB)微小缺陷检测样本少、效率低、检测算法参数量大和精度低的问题,本文基于YOLOv8n框架提出了一种轻量化检测模型PCB-YOLO。首先,对原始的多种颜色背景的大幅面PCB数据集进行数据预处理,通过灰度变换和切片操作得到适合YOLOv8输入尺寸的PCB数据和降低颜色背景干扰影响,防止PCB的微小缺陷在缩放的过程中丢失;其次,考虑到PCB存在小目标缺陷的占比较大问题,通过结合SimAM注意力机制和合并切片操作,设计Conv_SWS(SimAM with slicing)模块,替代传统的下采样方式,保留小目标的关键信息,从而增强PCB中微小目标缺陷的特征提取并提高检测准确性;然后,对YOLOv8中原有的C2f模块进行改造,引入部分卷积(PConv)对关键特征通道进行综合计算,设计FBC2f模块,用于增强特征提取能力并减少模型参数量;最后,采用基于Wasserstein距离的度量标准NWD(normalized Wasserstein distance)与标准IoU(intersection over union)结合改进损失函数来加速模型收敛,进一步提高YOLOv8对微小目标的检测能力。实验结果表明,与原始的YOLOv8n相比,在PCB缺陷检测数据集上的PCB-YOLO模型的参数减少了23.6%,mAP50提高了2.9%,达到95.6%。在工业铝片表面缺陷、PKU-Market-PCB缺陷数据集上的泛化性实验也验证了PCB-YOLO模型的有效性。
文摘印刷电路板(Printed Circuit Board,PCB)缺陷会造成巨额经济损失与安全隐患,传统的检测方法精度和效率都较为低下,现有的深度学习模型在面对复杂背景下的小目标检测时存在明显的不足。文章针对YOLOv10在PCB中的检测性能不足,在主干网络采用SPD-Conv模块替代传统卷积,通过维度重排保留小目标的特征并且降低背景干扰。在颈部网络的C2f模块中嵌入SE注意力机制,构建C2f_SE模块提升特征区分度。文章在北京大学PCB数据集的基础上,通过镜像、旋转等数据增强后将数据集扩展至6930张。实验结果表明,改进模型平均精度均值(mean Average Precision,mAP)达98.1%,较原始YOLOv10提升4.7%,其中鼠咬、毛刺等小目标缺陷检测精度提升明显。该模型为工业场景PCB缺陷检测提供了高效可靠方案。
文摘Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.
