Lightning Search Algorithm with Deep Transfer Learning-Based Vehicle Classification

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 manage-ment. 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 from small 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.