CNN-Based Automatic Helmet Violation Detection of Motorcyclists for an Intelligent Transportation System

An intelligent transportation system (ITS) is an advanced application that supports multiple transport and traffic management modes. ITS services include calling for emergency rescue and monitoring traffic laws with the help of roadside units. It is observed that many people lose their lives in moto...

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Bibliographic Details
Published in:Mathematical problems in engineering 2022-10, Vol.2022, p.1-11
Main Authors: Waris, Tasbeeha, Asif, Muhammad, Ahmad, Maaz Bin, Mahmood, Toqeer, Zafar, Sadia, Shah, Mohsin, Ayaz, Ahsan
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Cameras
Datasets
Deep learning
Helmets
Intelligent transportation systems
Literature reviews
Motorcycles
Neural networks
Real time
Rescue operations
Roads & highways
Roadsides
Surveillance
Traffic congestion
Traffic management
Transportation planning
Video
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Summary:An intelligent transportation system (ITS) is an advanced application that supports multiple transport and traffic management modes. ITS services include calling for emergency rescue and monitoring traffic laws with the help of roadside units. It is observed that many people lose their lives in motorbike accidents mainly due to not wearing helmets. Automatic helmet violation detection of motorcyclists from real-time videos is a demanding application in ITS. It enables one to spot and penalize bikers without a helmet. So, there is a need to develop a system that automatically detects and captures motorbikers without a helmet in real time. This work proposes a system to detect helmet violations automatically from surveillance videos captured by roadside-mounted cameras. The proposed technique is based on faster region-based convolutional neural network (R-CNN) deep learning model that takes video as an input and performs helmet violation detection to take necessary actions against traffic rule violators. Experimental analysis shows that the proposed system gives an accuracy of 97.69% and supersedes its competitors.
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/8246776