Vehicle Detection and Categorisation Using Deep Learning Algorithms

Abstract: This research addresses the crucial task of real-time vehicle detection and traffic analysis on highways and busy roads, employing advanced object detection algorithms & deep learning techniques. The study focuses on identifying various vehicle types, including cars, SUVs, bikes, buses, and trucks, using four distinct algorithms: Single Shot Multibox Detector (SSD), Kalman Filter Algorithm, You Only Look Once (YOLO v7), and Mask Regional-Convolutional Neural Network (Mask R-CNN). The main goal of the research is to apply these algorithms in real-world settings on busy metropolitan roads and highways for purpose of traffic analysis. The study software is designed to monitor traffic flow and count the number of cars that pass by in a given period of time, like a day, a week, or a month. Additionally, the algorithm sorts these cars into various categories and offers a thorough statistical breakdown of the normal vehicle composition on the road under observation. The research's conclusions help politicians, transportation engineers, and urban planners by providing insightful information about traffic patterns. These algorithms enable data- driven analysis, which in turn informs decisions on traffic management, road infrastructure, and safety protocols. Through the integration of state-of-the-art technology with practical applications, this research makes a substantial contribution to the improvement of traffic monitoring systems, thereby facilitating safer and more intelligent urban movemen