Research on automotive parts recognition based on improved YOLOv5 algorithm

With the continuous development of the automotive industry, the target detection and recognition of automotive parts have become crucial factors for automakers to enhance automation levels. In this paper, to ensure improved detection accuracy while meeting real-time detection requirements, we propose an enhanced YOLOv5 model for automotive parts recognition. This method optimizes candidate box parameters using the K-Means algorithm, addresses feature mismatch issues through variational convolution, and optimizes the loss function using GIOU instead of IOU. Experimental results show that the improved YOLOv5 algorithm achieves an average precision of 99.1%, representing a relative improvement of 1.72% over Fast R-CNN and 3.91% over the standard YOLOv5 algorithm. Furthermore, it demonstrates excellent real-time detection performance, meeting the demands of practical application scenarios.