IoT-Based Emergency Vehicle Detection Using YOLOv8
- Title
- IoT-Based Emergency Vehicle Detection Using YOLOv8
- Creator
- Suhana, Syed; Prathap, Boppuru Rudra; Narang, Kavish; Anto, Ivin
- Description
- The rapid response of emergency services plays a critical role in saving lives and minimizing the impact of emergencies. However, identifying and locating emergency vehicles in real-Time can be challenging, especially in congested urban areas. This paper focuses on the emergency vehicle identification using the You Only Look Once version 8 (YOLOv8) algorithm and is focused on Internet of Things (IoT). The goal of this research is to develop a real-Time and precise emergency vehicle detection system using You Only Look Once version 8 (YOLOv8) algorithm, trained and tested with a dataset from a camera placed on a busy road, to enhance emergency service response times. The findings demonstrate the suggested system's ability to recognize emergency vehicles at a speed of 31 frames per second and with a 95% accuracy rate. Modern object identification algorithms include the You Only Look Once version 8 (YOLOv8) algorithm, which has shown promising results in various applications. The proposed system is built on a Raspberry Pi, which acts as an edge device and processes the video stream in realtime. The system consists of an Internet of Things (IoT) device with a camera that captures the live video stream, which is then fed into the algorithm for object detection. Once an emergency vehicle is detected, the system sends an email notification to the nearby emergency services, like a police station, using Simple Mail Transfer Protocol (SMTP), who can then take appropriate action. The results of this investigation show that the Internet of Things and You Only Look Once version 8 (YOLOv8) algorithms have great promise for creating effective and dependable emergency vehicle detection systems. The proposed system possesses the capacity to save lives and improve the effectiveness of emergency response by speeding up response times for emergency services. The suggested solution is also inexpensive, simple to implement, and adaptable to existing infrastructure. Through the development of intelligent transportation systems, emergency services can operate more safely and effectively. More sophisticated machine learning algorithms may be incorporated into the proposed system, and further sensors can be added to utilize alternative methods beyond camera-based detection to identify emergency vehicles. Overall, this research shows the potential of Internet of Things (IoT) and machine learning in creating creative emergency services solutions. 2025 Syed Suhana et al.Published by Sciendo.
- Source
- Journal of Automation, Mobile Robotics and Intelligent Systems;Volume;19;Issue;2;pp.79-88
- Date
- 01-01-2025
- Publisher
- Sciendo
- Subject
- email notifications; emergency vehicles; Internet of Things (IoT); Pi Camera; Raspberry Pi; simple mail transfer protocol (SMTP); You Only Look Once (YOLOv8)
- Coverage
- Suhana S., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), India; Prathap B.R., Department of Computer Science and Engineering, M. S. Ramaiah University of Applied Sciences, India; Narang K., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), India; Anto I., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), India
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 18978649;
- Format
- online
- Language
- English
- Type
- Article
Collection
Citation
Suhana, Syed; Prathap, Boppuru Rudra; Narang, Kavish; Anto, Ivin, “IoT-Based Emergency Vehicle Detection Using YOLOv8,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23237.
