Solder Joint Detection of Automobile Body in White Based on Improved YOLOv5s
Aiming at the problem of missed detection and error detection caused by the irregularity of solder joints and the vulnerability to environmental light in the traditional machine vision-based automobile solder joint detection,this paper proposes an improved YOLOv5s algorithm YOLOv5s_CB_SI.The algorithm improves the model attention to the important area information of the image and the learning ability of the target defect by introducing the CBAM convolution attention mechanism in the backbone network Backbone,and introduces the SIoU positioning loss function to accelerate the convergence speed of the model,and effectively improve the model detection and positioning ability.The improved algorithm is compared with the YOLOv5s method on the solder joint data set.The experimental results show that the accuracy and recall rate of the proposed algorithm are improved by 4.8％and 2％respectively com-pared with the original algorithm,which effectively improves the accuracy in the solder joint detection process.At the same time,the false detection rate is reduced,which proves the effectiveness of the improved model.
object detectionloss functionYOLOv5s algorithmattention mechanismrecognition of solder joints
万方数据
维普
