A 360-degree Multi-camera System for Blue Emergency Light Detection Using Color Attention RT-DETR and the ABLDataset

TL;DR

This paper introduces a multi-camera system utilizing advanced neural networks and color attention mechanisms for accurate detection and localization of emergency vehicle blue lights, enhancing ADAS and road safety.

Contribution

It develops a multi-camera detection system with a novel color attention RT-DETR model and a curated dataset, improving detection accuracy and localization of emergency lights.

Findings

Achieved 94.7% detection accuracy and 94.1% recall.

Field detection range up to 70 meters.

Demonstrated effective vehicle approach angle estimation.

Abstract

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.