Motorcycle Apprehension using Deep Learning and K-Nearest Neighbor Algorithm

Abstract

─ Road violations that lead to accidents and deaths are increasing significantly. There are about 1.35 million people who die every year because of road accidents, and more than half of these involve a motorcycle. Authorities are strictly implementing traffic laws and making some innovations to capture those motorists violating laws easily. Researchers are also doing their part to help solve the problem; indeed, their studies give a vast contribution and solve road safety issues. However, the papers on road violations were focused more on on-road violations involving four-wheeled vehicles. For this reason, a motorcyclist violation detection and plate recognition with e-mail notification using a Deep Learning algorithm were developed to apprehend motorcyclists violating traffic laws. Tensorflow Object Detection API was used as a framework along with the Faster R-CNN model. The system was developed using Anaconda Environment, Python Scripting, KNN, and MySQL Connector. The conditions and criteria for detecting a violation are based on motorcycle detection, including motorcycle tracking. After violation detection and plate recognition, the violation's image is sent through e-mail together with the details of the offense.