[2603.05058] A 360-degree Multi-camera System for Blue Emergency Light Detection Using Color Attention RT-DETR and the ABLDataset

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.05058 (cs) [Submitted on 5 Mar 2026] Title:A 360-degree Multi-camera System for Blue Emergency Light Detection Using Color Attention RT-DETR and the ABLDataset Authors:Francisco Vacalebri-Lloret (1), Lucas Banchero (1), Jose J. Lopez (1), Jose M. Mossi (1) ((1) Universitat Politècnica de València, Spain) View a PDF of the paper titled A 360-degree Multi-camera System for Blue Emergency Light Detection Using Color Attention RT-DETR and the ABLDataset, by Francisco Vacalebri-Lloret (1) and 3 other authors View PDF HTML (experimental) 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, th...