Pin Solder Spot Detection Algorithm Based on Improved YOLOv5s

In this paper, an algorithm based on improved YOLOv5s is proposed to improve the accuracy and efficiency of pin solder spot detection. First, we employ traditional data enhancement strategies, including transformations such as rotation, translation, and scaling, to increase the generalization ability of the model. Secondly, in the neck network, we improve the ability of the model to extract solder joint features by adjusting the parameters of the convolutional layer and increasing the ECA (efficient channel attention) attention mechanism. Finally, in the Head part, we introduce Focal Loss and Bounding Box Regression Loss to better deal with class unbalance and boundary frame positioning problems in welding spot detection. Through the optimization of YOLOv5s network structure and the improvement of detection principle, the performance of the algorithm in the task of pin solder joint detection has been significantly improved. The experimental results show that the improved algorithm performs well on the data set, the recall rate of the improved algorithm is 99.5%, and the mAP (mean average accuracy) value is 97.35%, which has high accuracy and robustness.