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.
