Designing of VehiNet Using Convolutional Neural Networks and Deep Learning Techniques

作     者：Mahita Kandala Kaushik M Vaishakh Nambiar Vignesh G S Jyotsna C. Tripty Singh Prakash Duraisamy 

作者机构：Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetam India University of Wisconsin Green Bay Wisconsin USA 

出 版 物：《Procedia Computer Science》 

年 卷 期：2024年第235卷

页      面：1409-1418页

主　　题：Traffic Congestion Vehicle Detection Convolutional Neural Network Recurrent Neural Network Deep Learning Classification 

摘      要：Traffic congestion has become a critical issue in metropolitan areas, inflicting significant hardships on individuals on a daily basis. This adverse situation poses multifaceted challenges, including increased air pollution, heightened fuel consumption, and compromised road safety. Deep learning algorithms, encompassing both Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) have showcased remarkable performance in areas such as computer vision, natural language processing, healthcare, finance, and autonomous systems. This research paper proposes a novel methodology to develop a vehicle detection model - VehiNet, which can subsequently be refined to create an automated traffic signal system. The study explores the effectiveness of various CNN, RNN, and deep learning algorithms and techniques in accomplishing this objective. The dataset picked for the study in addition to being compiled from internet resources, was also painstakingly assembled manually using a variety of methods to collect over 2000 photographs from various angles, lighting conditions, and locations within Bengaluru city. The main motive is to apply CNN, RNN, and other Deep Learning classification techniques for object detection and classification of Indian vehicles and see how well these deep learning algorithms and techniques affect prediction efficiency. This research stands out from others and makes its novelty clear thanks to the carefully collected dataset and the detailed analysis of the methodologies.