Defect detection of small object solder joints based on improved YOLOv7
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.
国家科技期刊平台
NSTL
万方数据
