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

2026-03-05

AI Summary

This study addresses the challenge of detecting emergency vehicle blue lights and estimating their approach angle under complex climatic and geographic conditions. The authors propose a 360° visual system based on a quad-fisheye-camera setup, supported by a newly constructed multi-scenario dataset named ABLDataset. They enhance the RT-DETR model with a color attention mechanism specifically tuned for blue-light detection and integrate camera calibration with geometric transformation to estimate the azimuth angle. Evaluated on the test set, the method achieves 94.7% precision and 94.1% recall, with an effective real-world detection range of up to 70 meters—significantly outperforming baseline detectors such as YOLO variants, RetinaNet, and Faster R-CNN. This approach provides a robust visual input for multimodal advanced driver-assistance systems (ADAS).

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: State Estimation
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Security and Privacy: Large-scale security measurements

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.

Problem

• blue emergency light detection
• 360-degree multi-camera system
• emergency vehicle detection
• azimuthal localization
• ADAS

Innovation

• Color Attention RT-DETR
• 360-degree Multi-camera System
• Blue Light Detection
• ABLDataset
• Azimuthal Localization