IoT Based Emergency Vehicle Detection using YOLOv8

Abstract

The Research focuses on the real-time identification of emergency vehicles using the YOLOv8 algorithm in the context of IoT. The aim is to develop an efficient and accurate emergency vehicle detection system to improve emergency service response times. The proposed system utilizes the YOLOv8 algorithm trained and tested with a dataset from a camera placed on a busy road. The results demonstrate that the system can detect emergency vehicles at a speed of 31 frames per second with a 95% accuracy rate. The system is implemented using a Raspberry Pi as an edge device, processing the live video stream from an IoT device equipped with a camera. Once an emergency vehicle is detected, an alert is sent to the emergency services for prompt action. The study highlights the potential of the YOLOv8 algorithm and IoT in creating effective and reliable emergency vehicle detection systems. The proposed solution is cost-effective, easy to implement, and adaptable to existing infrastructure. It has the capability to save lives and enhance emergency response by reducing response times. Future improvements can include the incorporation of more advanced machine learning algorithms and additional sensors to identify other emergency vehicles like ambulances and fire engines. The research emphasizes the potential of IoT and machine learning in developing innovative solutions for emergency services, particularly in the realm of intelligent transportation systems.