Smart Accident and Helmet Detection – Emergency Alert System
G. Saranya, S Ajaikrishna, Indhumathi Gopal, D Dhanush, S Chandiran · 2024
In order to improve safety and compliance in a variety of contexts, this system suggests a real-time accident and helmet detection system. The technology employs convolutional neural networks (CNNs) and the YOLOv5 algorithm to accurately identify helmet wear and recognize accidents. The model is deployed on surveillance cameras on roads and building sites, having been trained on a large dataset. It continuously monitors live video streams and alerts pertinent parties to occurrences and helmet violations. Enforcement of traffic laws and enhancement of road safety depend heavily on the detection of helmets and vehicle number plates. With its impressive speed and accuracy, the system incorporates CNNs into the YOLOv5 architecture, which makes it appropriate for traffic monitoring and surveillance systems. The system immediately generates emergency notification messages with geolocation information, including latitude and longitude coordinates, as soon as an accident is detected. Fast emergency response and aid are made possible by this, improving general safety precautions. Test results show that the system outperforms the state-of-the-art methods now available for helmet and license plate recognition, offering a reliable way to improve safety and compliance in a variety of settings. This novel method of traffic monitoring is a major development in the realm of road safety