Abstract:

Multi-class traffic participant detection in dense traffic scenarios remains a challenging visual task， which is crucial for traffic management and safety. To address this， a deep neural network-based detection algorithm， DSODet， is proposed to handle the challenges of partial occlusion and small-scale targets in dense traffic environment. Firstly， a lightweight CSPDarkNet network is used to extract features from traffic images. Then， a multi-scale feature fusion upsampling module is designed to enhance the representation capability for hard-to-detect targets. Next， a high-resolution detection branch is incorporated to improve detection accuracy for small-scale targets. Finally， a histogram feature distillation training method is proposed， which effectively guides the student model's training by minimizing the intersection ratio of feature histograms between the teacher and student models at corresponding layers， thus enabling parameter optimization and model compression. The experimental results show that DSODet achieves an average detection accuracy of 66.9% for traffic participants and 13.0% for small targets with partial occlusion， outperforming current state-of-the-art algorithms. The model contains only 2.9 M parameters， demonstrating its friendliness for edge device. The related code will be shared at https：//github.com/XMUT-Vsion-Lab.

Key words: object detection, dense traffic, small-scale targets, partial occlusion, histogram feature distillation