Hu Jun, Zheng Jiahao, Wan Wenwei, Zhou Yongqi, Huang Zhikai
School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China.
Sensors (Basel). 2025 May 26;25(11):3327. doi: 10.3390/s25113327.
With the rapid acceleration of urbanization and the increasing volume of road traffic, emergency vehicles frequently encounter congestion when performing urgent tasks. Failure to yield in a timely manner can result in the loss of critical rescue time. Therefore, this study aims to develop a lightweight and high-precision RT-DETR-EVD emergency vehicle detection model to enhance urban emergency response capabilities. The proposed model replaces ResNet with a lightweight CSPDarknet backbone and integrates an innovative hybrid C2f-MogaBlock architecture. A multi-order gated aggregation mechanism is introduced to dynamically fuse multi-scale features, improving spatial-channel feature representation while reducing the number of parameters. Additionally, an Attention-based Intra-scale Feature Interaction Dynamic Position Bias (AIDPB) module is designed, replacing fixed positional encoding with learnable dynamic position bias (DPB), improving feature discrimination in complex scenarios. The experimental results demonstrate that the improved RT-DETR-EVD model achieves superior performance in emergency vehicle detection under the same training conditions. Specifically, compared to the baseline RT-DETR-r18 model, RT-DETR-EVD reduces parameter count to 14.5 M (a 27.1% reduction), lowers floating-point operations (FLOPs) to 49.5 G (a 13.2% reduction), and improves precision by 0.5%. Additionally, recall and mean average precision (mAP50%) increase by 0.6%, reaching an mAP50% of 88.3%. The proposed RT-DETR-EVD model achieves a breakthrough balance between accuracy, efficiency, and scene adaptability. Its unique lightweight design enhances detection accuracy while significantly reducing model size and accelerating inference. This model provides an efficient and reliable solution for smart city emergency response systems, demonstrating strong deployment potential in real-world engineering applications.
随着城市化进程的快速加速和道路交通流量的不断增加，应急车辆在执行紧急任务时经常遇到拥堵情况。不能及时避让会导致关键救援时间的损失。因此，本研究旨在开发一种轻量级、高精度的RT-DETR-EVD应急车辆检测模型，以增强城市应急响应能力。所提出的模型用轻量级的CSPDarknet骨干网络取代了ResNet，并集成了创新的混合C2f-MogaBlock架构。引入了多阶门控聚合机制来动态融合多尺度特征，在减少参数数量的同时提高空间通道特征表示。此外，设计了一种基于注意力的尺度内特征交互动态位置偏差（AIDPB）模块，用可学习的动态位置偏差（DPB）取代固定的位置编码，提高复杂场景下的特征辨别能力。实验结果表明，改进后的RT-DETR-EVD模型在相同训练条件下的应急车辆检测中取得了优异的性能。具体而言，与基线RT-DETR-r18模型相比，RT-DETR-EVD将参数数量减少到1450万个（减少了27.1%），将浮点运算（FLOPs）降低到495亿次（减少了13.2%），并将精度提高了0.5%。此外，召回率和平均精度均值（mAP50%）提高了0.6%，达到了88.3%的mAP50%。所提出的RT-DETR-EVD模型在准确性、效率和场景适应性之间实现了突破性的平衡。其独特的轻量级设计提高了检测精度，同时显著减小了模型大小并加快了推理速度。该模型为智慧城市应急响应系统提供了一种高效可靠的解决方案，在实际工程应用中展现出强大的部署潜力。
