Lightning Search Algorithm with Deep Transfer Learning-Based Vehicle Classification  

作　　者：Mrim M.Alnfiai 

机构地区：[1]Department of Information Technology,College of Computers and Information Technology,Taif University,P.O.Box 11099,Taif,21944,Saudi Arabia

出　　处：《Computers, Materials & Continua》2023年第3期6505-6521,共17页计算机、材料和连续体（英文）

摘　　要：There is a drastic increase experienced in the production of vehicles in recent years across the globe.In this scenario,vehicle classification system plays a vital part in designing Intelligent Transportation Systems(ITS)for automatic highway toll collection,autonomous driving,and traffic management.Recently,computer vision and pattern recognition models are useful in designing effective vehicle classification systems.But these models are trained using a small number of hand-engineered features derived fromsmall datasets.So,such models cannot be applied for real-time road traffic conditions.Recent developments in Deep Learning(DL)-enabled vehicle classification models are highly helpful in resolving the issues that exist in traditional models.In this background,the current study develops a Lightning Search Algorithm with Deep Transfer Learning-based Vehicle Classification Model for ITS,named LSADTL-VCITS model.The key objective of the presented LSADTL-VCITS model is to automatically detect and classify the types of vehicles.To accomplish this,the presented LSADTL-VCITS model initially employs You Only Look Once(YOLO)-v5 object detector with Capsule Network(CapsNet)as baseline model.In addition,the proposed LSADTL-VCITS model applies LSA with Multilayer Perceptron(MLP)for detection and classification of the vehicles.The performance of the proposed LSADTL-VCITS model was experimentally validated using benchmark dataset and the outcomes were examined under several measures.The experimental outcomes established the superiority of the proposed LSADTL-VCITS model compared to existing approaches.

关 键 词：Intelligent transportation system object detection vehicle classification deep learning machine learning 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]