用四鱼眼相机和颜色注意力模型，实现70米外蓝光警车精准定位。
A 360-degree Multi-camera System for Blue Emergency Light Detection Using Color Attention RT-DETR and the ABLDataset
- 四鱼眼相机+颜色注意力增强的RT-DETR，实现360度覆盖。
- 在测试集上准确率94.7%，召回率94.1%，远距离检测达70米。
- 可估算警车接近角度，适合集成到智能驾驶安全系统中。
本研究提出一种用于检测应急车辆蓝色灯光的先进系统，基于ABLDataset数据集构建，该数据集包含欧洲多种气候与地理条件下应急车辆的图像。系统采用四个广角180度鱼眼摄像头，安装于车辆侧面，通过标定实现检测结果的方位定位。对YOLO（v5、v8、v10）、RetinaNet、Faster R-CNN及RT-DETR等主流深度神经网络进行了对比分析，最终选用RT-DETR作为基础模型，并引入颜色注意力模块，使测试集上的准确率达94.7%，召回率达94.1%，实地测试检测距离可达70米。此外，系统通过几何变换估计应急车辆相对于车辆中心的接近角度。该系统设计用于与视觉和声学数据融合的多模态系统，展现出高效率，为提升高级驾驶辅助系统（ADAS）与道路安全提供了有前景的解决方案。
原文摘要 · Abstract (English)
This study presents an advanced system for detecting blue lights on emergency vehicles, developed using ABLDataset, a curated dataset that includes images of European emergency vehicles under various climatic and geographic conditions. The system employs a configuration of four fisheye cameras, each with a 180-degree horizontal field of view, mounted on the sides of the vehicle. A calibration process enables the azimuthal localization of the detections. Additionally, a comparative analysis of major deep neural network algorithms was conducted, including YOLO (v5, v8, and v10), RetinaNet, Faster R-CNN, and RT-DETR. RT-DETR was selected as the base model and enhanced through the incorporation of a color attention block, achieving an accuracy of 94.7 percent and a recall of 94.1 percent on the test set, with field test detections reaching up to 70 meters. Furthermore, the system estimates the approach angle of the emergency vehicle relative to the center of the car using geometric transformations. Designed for integration into a multimodal system that combines visual and acoustic data, this system has demonstrated high efficiency, offering a promising approach to enhancing Advanced Driver Assistance Systems (ADAS) and road safety.
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