Automatic Traffic Rules Violation Detection and Number Plate Recognition System for Bangladesh

Abstract

The traffic controlling system in Bangladesh has
not been updated enough with respect to fast improving
technology. As a result, traffic rules violation detection and
identification of the vehicle has become more difficult as the
number of vehicles is increasing day by day. Moreover,
controlling traffic is still manual. To solve this problem, the
traffic controlling system can be digitalized by a system that
consists of two major parts which are traffic rules violation
detection and number plate recognition. In this research, these
processes are done automatically which is based on machine
learning, deep learning, and computer vision technology. Before
starting this process, an object on the road is identified through
the YOLOv3 algorithm. By using the OpenCV algorithm, traffic
rules violation is detected and the vehicle that violated these rules
is identified. To recognize the number plate of the vehicle, image
acquisition, edge detection, segmentation of characters is done
sequentially by using Convolution Neural Network (CNN) in
MATLAB background. Among the traffic rules, the following
traffic signal is implemented in this research.