Research on complex road condition target detection algorithm based on improved YOLOv8

Currently, there are several issues with complicated road conditions, including significant variations in the size of the vehicle target, dense occlusion, high overlap rate, and inadequate lighting. The results of target detection are imprecise, prone to error, and misinterpreted. To address these issues, this paper proposes a technique for target detection using multi-scale fusion with an improved version of YOLOv8. Firstly, a new feature fusion network based on the weighted bidirectional feature network and the adaptive spatial feature fusion is proposed to strengthen the feature extraction and improve the visual representation ability. Secondly, to further improve the ability to detect small targets, a multi-scale target detection layer is incorporated. Meanwhile, an enhanced CIOU loss function is presented to accelerate the convergence of the model. Finally, this article combines ordinary convolution with depth separable convolution to improve the spatial feature information extraction capability and strengthen the model's generalization ability. Experiments were conducted on the KITTI dataset, and the results showed that the mAP of the improved network improved to 93.0%, which exceeded that of the traditional target detection network in terms of accuracy, parameters, and other key metrics. It is more suitable for instances involving autonomous driving when it comes to target detection.