TrafficMon No Helmet

The primary objective of the No Helmet Detection System is to ensure the safety of individuals wearing helmets. Vehant’s no helmet detection system utilizes deep learning to automate helmet detection using trained models and datasets, benefiting various sectors including public authorities for law enforcement and safety compliance. The head is a critical body part, and wearing a helmet is essential for safety against severe injuries or fatalities. An automated no helmet detection system can differentiate between individuals wearing helmets and those who are not. Vehant’s No Helmet Detection system is designed to assist public authorities in law enforcement and contribute to societal welfare, while also aiding industries like construction and hospitality in following regulations.

Benefit for Public Authorities

With significant increase in the number of two-wheelers used, people are more prone to accidents. A system that can act as a measure to curb such happening is a must for the authorities to enforce law and order effectively and ensure public safety. Helmet is the primary security measure for all riders however it is not utilized properly. An automated system can be a one-stop solution for motorcycle detection and classification on public roads and for automatic detection of motorcyclists and bicycle riders without helmet. The No Helmet Detection System by Vehant uses traffic images captured by cameras in real time and is capable of classifying the violators accurately.

Benefit for Construction Industry

Some of the major responsibilities for construction industry include safety management, compliance with regulations and reducing the death rate from accidents at construction sites. Detecting safety helmet-wearing in surveillance videos is an essential task for the industry to ensure those responsibilities. Vehant’s no helmet detection system makes use of neural network-based detection to recognize whether a person is wearing a helmet or not even from far. No helmet detection system uses images from surveillance cameras in real time and analyzes them for detecting helmet on head using computer vision and machine learning techniques.