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The IUP Journal of Telecommunications

Detecting and Classifying Vehicles Under Uncontrolled Environmental Conditions: A Transfer Learning-Based Approach✝

Article Details

Pub. Date	: Nov' 2023
Product Name	: The IUP Journal of Telecommunications
Product Type	: Article
Product Code	: IJTC021123
Author Name	: Agha Asim Husain, Tanmoy Maity, R K Yadav and Mritunjay Rai
Availability	: YES
Subject/Domain	: Arts & Humanities
Download Format	: PDF Format
No. of Pages	: 11

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Abstract

Vehicle detection and classification is an active area of research in vehicle surveillance system and has numerous applications in intelligent transportation system. In vehicle surveillance system, identifying the make and model of a vehicle is crucial for traffic monitoring. Due to intraclass diversity, viewpoint variation, and variable illumination conditions, identifying vehicle make and model is a difficult process. Earlier researchers have used different datasets for the detection and classification of vehicle as per their make and model. However, a majority of these studies have used datasets of clear high-quality images. The present study uses dataset with vehicle images taken under uncontrolled environmental conditions such as images with different illumination, shadowing, reflection from vehicle surface, etc. and employs transfer learning approach to develop an efficient algorithm for computer vision based on traffic surveillance system that can detect and classify vehicles. The study further compares and analyzes the accuracy and precision of the two transfer learning techniques. The study deploys machine learning (ML) classifiers for classifying the vehicles as per their models.

Introduction

Deep learning (DL)1 (Kim et al., 2019) algorithms have significantly improved over previous methods that rely on the retrieval of key characteristics in several computer vision problems, including object identification, location, partitioning and classification. In addition, DL networks like CNN (Hussain et al., 2018) have demonstrated impressive prospects in video surveillance (Rai et al., 2019) and computer vision identification applications. They have excelled in decision making in numerous tasks by automatically revealing intriguing insights from visual attributes with a high degree of certainty.