An improved YOLOv8 algorithm for small object detection in autonomous driving

In the task of visual object detection for autonomous driving, several challenges arise, such as detecting densely clustered targets, dealing with significant occlusion, and identifying small-sized targets. To address these challenges, an improved YOLOv8 algorithm for small object detection in autonomous driving (MSD-YOLO) is proposed. This algorithm incorporates several enhancements to improve the performance of detecting small and densely occluded targets. Firstly, the downsampling module is replaced with SPD-CBS (Space-to-Depth) to maintain the integrity of channel feature information. Subsequently, a multi-scale small object detection structure is designed to increase sensitivity for recognizing densely packed small objects. Additionally, DyHead (Dynamic Head) is introduced, equipped with simultaneous scale, spatial, and channel attention to ensure comprehensive perception of feature map information. In the post-processing stage, Soft-NMS (non-maximum suppression) is employed to effectively suppress redundant candidate boxes and reduce the missed detection rate of densely occluded targets. The effectiveness of these enhancements has been verified through various experiments conducted on the BDD100K autonomous driving public dataset. Experimental results indicate a significant improvement in the performance of the enhanced network. Compared to the YOLOv8n baseline model, MSD-YOLO shows a 13.7% increase in mAP50 and a 12.1% increase in mAP50:95, with only a slight increase in the number of parameters. Furthermore, the detection speed can reach 67.6 FPS, achieving a better balance between accuracy and speed.