Smart Motorcycle Helmet: A CNN-Based Multi-Task Learning Approach

Abstract

Automated motorcycle helmet detection through video surveillance is crucial for enhancing road safety through education and enforcement. Existing approaches face limitations, such as difficulty in tracking individual motorcycles and distinguishing drivers from passengers. To address these challenges, we propose a CNN-based multi-task learning (MTL) method for identifying and tracking individual motorcycles, with a focus on rider-specific helmet use. We introduce the HELMET dataset, comprising 91,000 annotated frames from 10,006 motorcycles across 12 observation sites in Myanmar, providing a benchmark for future detection approaches. Our MTL approach, leveraging concurrent visual similarity learning and helmet use classification, achieves improved efficiency and accuracy. The proposed method operates at over 8 FPS on consumer hardware, yielding a weighted average F-measure of 67.3% for detecting riders and helmet use. Our work showcases deep learning as an accurate and resource-efficient means of collecting critical road safety data. we present an intelligent motorcycle helmet featuring infrared transceivers, an image sensor, an embedded computation module, a charging module, a microphone, and earphones. Designed for large vehicle approach intimation, the helmet uses image recognition modes for day and night conditions. Experimental results demonstrate successful vehicle registration plate recognition for large trucks/buses, achieving up to 75% accuracy during the day and 70% at night. The proposed intelligent motorcycle helmet detects approaching large vehicles in real time within a 5-meter distance.