Smart Motorcycle Helmet: Utilizing CNN for Multi-Task Learning

Abstract

Enhancing road safety through education and enforcement relies on automated motorcycle helmet detection via video surveillance. However, existing methods encounter challenges like tracking individual motorcycles and distinguishing riders from passengers. To overcome these limitations, we propose a CNN-based multi-task learning (MTL) method. Our approach focuses on identifying and tracking individual motorcycles, particularly emphasizing rider-specific helmet use. We introduce the HELMET dataset, containing 91,000 annotated frames from 10,006 motorcycles across 12 observation sites in Myanmar, serving as a benchmark for future detection techniques. Leveraging concurrent visual similarity learning and helmet use classification, our MTL approach achieves enhanced efficiency and accuracy, operating at over 8 FPS on consumer hardware. With a weighted average F-measure of 67.3% for detecting riders and helmet use, our method underscores deep learning's accuracy and resource efficiency in gathering critical road safety data. Furthermore, we present an intelligent motorcycle helmet equipped with infrared transceivers, an image sensor, an embedded computation module, a charging module, a microphone, and earphones. Designed for large vehicle approach notification, the helmet utilizes image recognition modes for both 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. In real-time, the proposed intelligent motorcycle helmet detects approaching large vehicles within a 5-meter distance.