Improving the Vehicle Small Object Detection Algorithm of Yolov5

To address the problems of low accuracy and poor robustness in vehicle small object detection for autonomous driving tasks, this study aims to propose an improved vehicle small object detection algorithm model based on YOLOv5. Firstly, some convolutions in the backbone network are replaced with receptive field attention convolutions, and the weights of the convolution kernels are dynamically assigned based on the importance of image features to ensure the extraction of important features. Secondly, adding a channel attention mechanism to the backbone network enhances the attention to small target features. Finally, the Focal-EIoU loss function is introduced to increase the attention on high-quality samples in the regression stage of object detection boxes. When the model is applied to the small object test set of the KITTI dataset, the precision rate, recall rate and mean average precision are 88.5%, 82.8%, and 84.9%, respectively, and the frame processing rate reaches 87.83FPS.