Motorcycle Rider Helmet Detection for Riding Safety and Compliance Using Convolutional Neural Networks
Nemuel Norman F. Giron, Robert Kerwin C. Billones, Alexis M. Fillone, Jay Robert Del Rosario, Melvin K. Cabatuan, Argel Alejandro Bandala, Elmer P. Dadios · 2020
Traffic violation apprehension is one of the traffic problems here in the Philippines. One example is the No Helmet No Ride Law that is implemented but many motorists still choose to ignore. To alleviate the problem the government has offered many solutions, one of which is the No Contact Traffic Apprehension Policy that uses CCTV Monitoring. To further enhance this solution the government has partnered with the De La Salle University to use artificial intelligence in the system. Computer Vision tasks like image classification and object detection can help automate the traffic apprehension system. Image classification and object detection are technologies which are used in computer vision in defining an image or coordinates of an object in an image. In this work, a novel approach to classifying motorcycle riders between wearing a helmet or not will be developed. It will be demonstrated using deep machine learning, specifically convolutional neural network and by utilizing different pre-trained models to a gathered dataset.