<![CDATA[Purpose – Delays experienced by ambulances at signalized intersections remain a critical issue in emergency transportation, particularly in dense urban traffic conditions. This study aims to develop and evaluate a low-cost real-time ambulance detection system using Raspberry Pi 4B and YOLOv5 to support intelligent transportation monitoring and emergency vehicle prioritization. Design/methods/approach – This study employed an experimental research design by integrating CCTV cameras, Raspberry Pi 4B, YOLOv5s object detection, ONNX Runtime INT8 optimization, and Telegram Bot API notification. The model was trained using 3,250 annotated ambulance images divided into training, validation, and testing subsets. System performance was evaluated under five operational scenarios: daytime, nighttime, heavy traffic, long-distance detection, and low-lighting conditions. Findings – The proposed YOLOv5s model achieved precision of 95.4%, recall of 93.8%, mAP@0.5 of 96.1%, and sustained throughput of 22 FPS on Raspberry Pi 4B. The Telegram notification subsystem achieved a transmission success rate of 98.7% with an average delay of 1.8 seconds. However, detection performance decreased under low-lighting conditions, with a true positive rate of 78.5% and false positive rate of 11.2%. Research implications/limitations – The system demonstrates the feasibility of deploying embedded computer vision for cost-effective ambulance detection, although nighttime reliability and traffic signal integration require further improvement. Originality/value – This study contributes an ONNX INT8-optimized YOLOv5s implementation on Raspberry Pi 4B with multi-condition evaluation and real-time Telegram notification for ambulance detection at traffic intersections.]]>
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