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Multiclass 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 networkbased detection algorithm, DSODet, is proposed to handle the challenges of partial occlusion and smallscale targets in dense traffic environment. Firstly, a lightweight CSPDarkNet network is used to extract features from traffic images. Then, a multiscale feature fusion upsampling module is designed to enhance the representation capability for hardtodetect targets. Next, a highresolution detection branch is incorporated to improve detection accuracy for smallscale 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 stateoftheart 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/XMUTVsionLab.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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