Abstract: With the rapid development of autonomous driving technology, road traffic detection, as a core task of the perception module, directly impacts the safety and reliability of autonomous driving systems. Although deep learning-based methods have become a research hotspot, challenges such as low detection accuracy and poor model generalization remain. To address these issues, this paper proposes an improved YOLO11n-based road traffic detection method. The proposed approach enhances the detection accuracy of small objects by adding a small object detection layer, optimizes the existing dual DWConv structure by introducing a GhostConv+DWConv detection head combination, and designs an Inner-CIoU loss function better suited for small objects to improve model generalization and the accuracy of bounding box regression. Experimental results show that, compared with the existing YOLO11n algorithm, the proposed model achieves detection accuracy improvements of 1.1% and 1.9% on the KITTI and BDD100K datasets, respectively, with detection speeds of 125 FPS and 124 FPS. This demonstrates the model’s effectiveness in detecting low-resolution small objects and its strong generalization capability across diverse traffic scenarios.

Key words: autonomous driving, small target detection, YOLO11, multi-scale detection, loss function