基于改进YOLOv5的印刷电路板缺陷检测

为解决印刷电路板缺陷检测中缺陷类别易混淆，缺陷目标微小难以检测的问题，提出了一种改进的YOLOv5检测模型。在骨干网络引入Swin-Transformer架构，获取局部和全局信息的多尺度特征。增加一个针对小目标的预测特征层，新的多尺度特征融合和检测结构使模型学习更加全面的特征信息。使用ECIoU＿Loss作为损失函数，实现电路板缺陷检测速度和准确率协同优化。实验结果表明，改进后的YOLOv5模型在PCB Defect数据集上的平均准确率为98.7%,达到了99.7%的预测精确率和97.4%的召回率，比当前主流的检测模型性能更优越，改进后的YOLOv5模型能更有效的对电路板缺陷进行分类和定位。

Printed circuit board defect detection based on improved YOLOv5

To address the challenges of confusion among defect categories and the difficulty in detecting small defect targets in printed circuit board defect detection, an improved YOLOv5 detection model was proposed. The Swin-Transformer is incorporated into the backbone network to extract multi-scale features, capturing both local and global information. A prediction feature layer is added specifically for small targets, and the new multi-scale feature fusion and detection structure enable the model to learn more comprehensive feature information. The ECIoU_Loss is employed as the loss function, facilitating collaborative optimization between detection speed and accuracy in circuit board defect detection. Experimental results demonstrate that the improved YOLOv5 model achieves a mean average precision of 98.7% on the PCB Defect dataset, with a precision of 99.7% and a recall of 97.4%. These performance metrics outperform current mainstream detection models, showcasing the improved YOLOv5 model''s effectiveness in classifying and localizing circuit board defects.