International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 11 | Nov 2025
p-ISSN: 2395-0072
www.irjet.net
SMART EMERGENCY RESPONSE SYSTEM FOR REAL TIME ACCIDENT DETECTION AND AUTOMATED REPORTING Chaitra K J1 ,Sunil L M2, Sooraj Kumar N R3, Sanjay M R4 ,Prajwal B M5 1 Assistant Professor ,Information Science and Engineering ,Bapuji Institute of Engineering and Technology, Karnataka,
India
2 Bachelor of Engineering, Information Science and Engineering, Bapuji Institute of Engineering and technology,
Karnataka, India
3 Bachelor of Engineering, Information Science and Engineering, Bapuji Institute of Engineering and technology,
Karnataka, India
4 Bachelor of Engineering, Information Science and Engineering, Bapuji Institute of Engineering and technology,
Karnataka, India
5 Bachelor of Engineering, Information Science and Engineering, Bapuji Institute of Engineering and technology,
Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------transportation systems that are capable of real-time Abstract - Road accidents continue to be one of the leading monitoring and automated decision-making. Leveraging these technologies, this project introduces a Smart Accident Detection and Alert System that utilizes the YOLOv8 deep learning model to identify accident scenarios from live CCTV feeds with high accuracy. The model is trained on a custom dataset of annotated accident frames, ensuring it can distinguish between normal traffic movement and collision events.
causes of fatalities and congestion in rapidly growing nations. Traditional accident detection methods relying on manual monitoring, on-vehicle sensors, or delayed public reporting often fail to provide timely alerts to emergency services, resulting in increased casualties and slower incident response. To address these challenges, this project presents an AI-driven Accident Detection and Alert System that uses the YOLOv8 object detection algorithm to analyze live CCTV footage for real-time accident identification. The system is trained using a custom dataset created from annotated accident video frames, enabling it to accurately detect collision events. Upon detection, an automated alert is sent instantly through the Twilio cloud communication API to emergency services such as ambulance, police, and fire departments. The integration of high-speed AI inference with cloud-based communication ensures rapid response, continuous monitoring, and minimal human involvement. Key Words: Accident detection, YOLOv8, Twilio, SMS alert, Object Detection
When the system detects an accident, it seamlessly integrates with the Twilio cloud communication API to deliver instant SMS notifications to emergency responders. This automated alert mechanism significantly reduces the dependency on human reporting and ensures that help is deployed in the shortest possible time. The system operates continuously, provides 24/7 monitoring, and can be scaled across urban and rural surveillance networks. By combining high-performance AI detection with reliable cloud communication, the proposed solution offers a modern approach to improving road safety and accelerating emergency response.
1.INTRODUCTION
2. PROPOSED SYSTEM
With the expansion of urban road networks and the increasing number of vehicles, road accidents have become a critical public safety concern. In countries experiencing rapid urbanization and economic growth, traffic density continues to rise, making timely accident detection more challenging. Conventional surveillance systems depend heavily on manual observation or limited hardware sensors, which often leads to delayed detection, missed incidents, and slower emergency response times. Such limitations significantly increase the risks associated with road accidents, including fatalities, severe injuries, and traffic disruptions.
The integrated system combines AI-powered accident detection with cloud-based automated alerting, creating a complete end-to-end emergency response pipeline.
Key Components
Advancements in artificial intelligence, deep learning, and computer vision have enabled the development of intelligent
© 2025, IRJET
|
Impact Factor value: 8.315
|
1.
Video Processing Unit: Traffic camera footage is continuously streamed into the system, where each video frame is converted into an analysable format using OpenCV.
2.
YOLOv8 Detection Model: YOLOv8 Trained on a custom accident dataset built from annotated CCTV accident footage. It detects vehicles, pedestrians, and crash patterns.
ISO 9001:2008 Certified Journal
|
Page 564