Real-Time Ambulance Detection on Resource-Constrained Edge Devices Using YOLOv5 and ONNX Runtime

Rapid response of Emergency Medical Services (EMS) is critically affected by urban traffic congestion, where conventional siren-based and manual traffic control mechanisms often fail to ensure timely right-of-way for ambulances. To address this limitation, this paper presents a real-time, vision-based ambulance detection system optimized for deployment on resource-constrained edge devices. The proposed system employs a fine-tuned YOLOv5s object detection model trained on a custom ambulance dataset and optimized using the Open Neural Network Exchange (ONNX) framework for efficient CPUbased inference. The optimized model is deployed on a Raspberry Pi (64-bit) platform using ONNX Runtime and integrated with a live IP camera stream for continuous detection. Experimental results demonstrate a Mean Average Precision (mAP@0.5) of 91.3% and a real-time inference speed of 2–5 FPS on the edge device. A comparative evaluation shows that ONNX Runtime significantly outperforms native PyTorch inference on the same hardware. The results demonstrate the practical feasibility of deploying a vision-based ambulance detection system on CPU-only edge devices without hardware accelerators, making the solution suitable for cost-sensitive Intelligent Transportation System deployments. The work primarily validates edge-level detection feasibility, with traffic control components evaluated via simulation. This work is positioned as an applied feasibility and deployment-oriented study rather than a novel algorithmic contribution. The study primarily evaluates deployment feasibility and runtime performance on CPU-only edge hardware.