Research on road target detection algorithm for autonomous driving based on improved YOLOv8

In order to solve the problems of missing detection and poor detection effect of small targets in autonomous driving scenarios, a road target detection algorithm with improved YOLOv8 algorithm was proposed. Firstly, the backbone network is replaced by FasterNet, which combines the multi-scale attention mechanism and depth separable convolution to improve the feature expression and receptive field range. Secondly, CBAM is integrated into the attention mechanism module, which combines the channel attention mechanism with the spatial attention mechanism to form a new convolutional block structure, so as to better carry out feature fusion. Finally, to solve the problem that CIOU loss function does not take into account the mismatch between the desired real frame and the predicted frame, Inner-SIoU loss function is introduced to effectively improve the accuracy of reasoning. Experimental results show that for the public Udacity data set, the proposed algorithm can improve the detection accuracy by2.9% while maintaining the same detection speed as the original algorithm.