Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning

TL;DR

This paper develops a real-time deep learning model using YOLOv5 and ensemble learning to detect helmet violations on motorbikes, demonstrating effectiveness in diverse conditions and potential for smart city traffic enforcement.

Contribution

It introduces a robust YOLOv5-based model trained with data augmentation for real-time helmet violation detection in challenging environments.

Findings

Achieved an mAP score of 0.5267 on test videos.

Ranked 11th on the AI City Track 5 leaderboard.

Proven potential for deployment in smart city traffic regulation enforcement.

Abstract

The proper enforcement of motorcycle helmet regulations is crucial for ensuring the safety of motorbike passengers and riders, as roadway cyclists and passengers are not likely to abide by these regulations if no proper enforcement systems are instituted. This paper presents the development and evaluation of a real-time YOLOv5 Deep Learning (DL) model for detecting riders and passengers on motorbikes, identifying whether the detected person is wearing a helmet. We trained the model on 100 videos recorded at 10 fps, each for 20 seconds. Our study demonstrated the applicability of DL models to accurately detect helmet regulation violators even in challenging lighting and weather conditions. We employed several data augmentation techniques in the study to ensure the training data is diverse enough to help build a robust model. The proposed model was tested on 100 test videos and produced an mAP score of 0.5267, ranking 11th on the AI City Track 5 public leaderboard. The use of deep learning techniques for image classification tasks, such as identifying helmet-wearing riders, has enormous potential for improving road safety. The study shows the potential of deep learning models for application in smart cities and enforcing traffic regulations and can be deployed in real-time for city-wide monitoring.