Need:
In India, two-wheelers account for a significant portion of road traffic and contribute heavily to the national burden of road fatalities. Despite regulatory mandates, helmet non-compliance remains widespread due to limited enforcement reach and behavioral inertia. Currently there is no intelligent system that enforces helmet usage autonomously in a closed loop directly at the vehicle level.
Motivation:
The existing enforcement strategies such as traffic policing or external camera-based surveillance are reactive, infrastructure-dependent, and ineffective at scale. To overcome these limitations, we propose a real-time helmet detection and closed-loop system that is integrated into the two-wheeler itself. This approach promotes proactive safety compliance without relying on external monitoring infrastructure.
Methodology:
The proposed model is AI-powered, vision-based system that leverages deep learning techniques for helmet detection, supported by a custom-built dataset accommodating cultural and regional variations.
Key challenges addressed in our approach include:
1. The system is trained to perform reliably under challenging conditions, including variable lighting, occlusions, and diverse headgear styles commonly seen in the Indian context.
2. It has been optimized for low-power, real-time inference suitable for embedded platforms on two-wheelers.
Upon detecting the absence of a helmet, the system initiates a two-stage response: an audible alert warns the rider, and if non-compliance persists, the vehicle enters a controlled deceleration mode through a closed-loop actuation strategy—bringing it to a safe stop.
Conclusions:
The preliminary evaluations indicate a detection accuracy of 97% under varied real-world conditions, establishing the feasibility of intelligent, vehicle-integrated enforcement for two-wheelers in the Indian context.
Keywords: Helmet Detection, Two-Wheeler Safety, Custom-built Dataset, Deep Learning, Closed-loop Control, Rider Monitoring, Real-time Compliance, Automotive AI, Indian Road Conditions.