RT-DETR-EVD:基于改进的RT-DETR的紧急车辆检测方法
Jun Hu1, Jiahao Zheng1, Wenwei Wan1
1School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
这项研究介绍了RT-DETR-EVD,一种轻量级的应急车辆检测模型. 它显著减少了模型大小,并提高了检测准确度,以更快地响应城市紧急情况.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 城市系统 城市系统
背景情况:
- 城市化和增加的交通拥堵对应急车辆的响应时间构成了挑战.
- 及时检测紧急车辆对于高效的救援行动至关重要.
研究的目的:
- 开发一种轻量级,高精度的应急车辆检测模型 (RT-DETR-EVD),以加强城市应急响应.
- 提高在拥挤环境中紧急车辆检测的效率和准确性.
主要方法:
- 开发了一个新的RT-DETR-EVD模型,使用轻量级的CSPDarknet骨干和混合C2f-MogaBlock架构.
- 集成了一个多顺序封闭的聚合机制,用于动态的多尺度特征融合.
- 引入了基于注意力的尺度内特征交互动态位置偏差 (AIDPB) 模块,以可学习的动态位置偏差 (DPB) 取代固定位置编码.
主要成果:
- 与基线RT-DETR-r18.18相比,RT-DETR-EVD降低了参数数量27.1% (至14.5M),FLOP降低了13.2% (至49.5G).
- 实现了0.5%的精度提高,回忆和mAP50%增加了0.6%,达到88.3%.
- 在紧急车辆检测方面表现出卓越的性能,提高了准确性,效率和场景适应性.
结论:
- 该RT-DETR-EVD模型为紧急车辆检测提供了准确性,效率和适应性之间的突破性平衡.
- 它的轻量级设计加速推断并减少模型大小,使其适合于现实世界智能城市应用.
- 为改善城市应急响应系统提供了高效可靠的解决方案.
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