Detecting Highway Code Violations using Artificial Intelligence

摘要

In this paper, we propose the use of autonomous law enforcement systems to aid in detecting highway code violations. Currently, the only way to enforce the law is to have an on-site official. This method tends to be very costly and allows multiple violations to go unnoticed. In turn, this reduces the efficacy of highway code enforcement. Automated law enforcement should fill this gap by reducing unnoticed contraventions which in turn contributes to safer driving and possibly reduces accidents. This research proposes the use of a Convolutional Neural Network (CNN) for object detection using two custom-made datasets for vehicles and STOP signs. This combination is used to identify a STOP sign and detect any vehicles passing over it without stopping. Our solution managed to detect 100% of STOP sign violations and non-violations in a real-life environment. The model for detecting the cars object and the STOP sign achieved an overall accuracy of 87.8% and 91.4% respectively.