基于改进YOLOv7的小目标焊点缺陷检测算法

Defect detection of small object solder joints based on improved YOLOv7

针对现有的小目标焊点缺陷检测方法存在错检、漏检等问题,提出一种改进YOLOv7的小目标焊点缺陷检测算法。考虑到焊点尺寸较小,添加小目标检测层和检测头以提取更多的浅层特征信息。引入无参注意力机制(SimAM)为特征图分配三维权重,提高模型特征提取能力。使用部分卷积(PConv)重构ELAN模块,减少冗余运算和内存访问次数,在颈部利用长径特征网络(GiraffeDet)融合不同尺度特征,提高模型轻量化程度。最后,利用NWD(Normalized Wasserstein Distance)损失函数改进原有的CIoU损失函数,加快模型收敛速度并提高小目标检测精度。实验结果证明,改进后的YOLOv7算法平均检测精度达到90.3%,相较于原算法提升了5.1%,召回率提高了3.2%,参数量降低了36.3%,且在收敛速度方面有了较大的提升。本文算法为边缘设备检测小目标焊点缺陷提供了参考。

Aiming at the problems of the existing small target solder joint defect detection methods,such as error detection and leakage detection,an improved YOLOv7 small target solder joint defect detection algorithm was proposed.Considering the small size of solder joints,a small target detection layer and detection head were added to extract more shallow feature information.The non-parametric attention mechanism(SimAM)was introduced to assign 3D weights to the feature graphs to improve the feature extraction ability of the model.Partial Convolution(PConv)was used to reconstruct ELAN modules to reduce redundant operations and memory access,and GiraffeDet was used to integrate different scale features at the neck to improve the lightweight of the model.Finally,the NWD(Normalized Wasserstein Distance)loss function was used to improve the original CIoU loss function,which sped up the convergence of the model and improved the detection accuracy of small targets.Experimental results show that the average detection accuracy of the improved YOLOv7 algorithm reaches 90.3%,which is 5.1%higher than that of the original algorithm.The recall rate is 3.2%higher,the number of parameters is 36.3%lower,and the convergence speed has been greatly improved.This algorithm provides a reference for detecting small target solder joint defects in edge equipment.