Flying foxes optimization with reinforcement learning for vehicle detection in UAV imagery

Abstract

Intelligent transportation systems (ITS) are globally installed in smart cities, which enable the next generation of ITS depending on the potential integration of autonomous and connected vehicles. Both technologies are being tested widely in various cities across the world. However, these two developing technologies are vital in allowing a fully automatic transportation system; it is necessary to automate other transportation and road components. Unmanned aerial vehicles (UAVs) or drones are utilized for many surveillance applications in the ITS. Detecting on-ground vehicles in drone images is significant for disaster rescue operations, traffic and parking management, and navigating uneven territories. This study presents a flying foxes optimization with deep learning-based vehicle detection and classification model on aerial images (FFODL-VDCAI) technique for ITS application. The main objective of the FFODL-VDCAI technique is to automate and accurately classify vehicles that exist in aerial images. Three primary processes are involved in the presented FFODL-VDCAI technique. Initially, the FFODL-VDCAI approach utilizes YOLO-GD (Ghost-Net and Depthwise convolution) for vehicle detection, where the YOLO-GD uses lightweight Ghost Net in place on the backbone network of YOLO-v4 and interchanges the conventional convolutional with depthwise separable convolutional and pointwise convolutional. Next, the FFO technique is used for hyperparameter tuning the Ghost Net technique. Finally, a deep Q-network (DQN) based reinforcement learning technique is used to classify detected vehicles effectively. A comprehensive simulation analysis of the FFODL-VDCAI methodology is conducted on the UAV image dataset. The performance validation of the FFODL-VDCAI methodology exhibited superior values of 96.15% and 92.03% under PSU and Stanford datasets concerning various aspects.