An intelligent water drop algorithm with deep learning driven vehicle detection and classification

Vehicle detection in Remote Sensing Images (RSI) is a specific application of object recognition like satellite or aerial imagery. This application is highly beneficial in different fields like defense, traffic monitoring, and urban planning. However, complex particulars about the vehicles and the surrounding background, delivered by the RSIs, need sophisticated investigation techniques depending on large data models. This is crucial though the amount of reliable and labelled training datasets is still a constraint. The challenges involved in vehicle detection from the RSIs include variations in vehicle orientations, appearances, and sizes due to dissimilar imaging conditions, weather, and terrain. Both specific architecture and hyperparameters of the Deep Learning (DL) algorithm must be tailored to the features of RS data and the nature of vehicle detection tasks. Therefore, the current study proposes the Intelligent Water Drop Algorithm with Deep Learning-Driven Vehicle Detection and Classification (IWDADL-VDC) methodology to be applied upon the Remote Sensing Images. The IWDADL-VDC technique exploits a hyperparameter-tuned DL model for both recognition and classification of the vehicles. In order to accomplish this, the IWDADL-VDC technique follows two major stages, namely vehicle detection and classification. For vehicle detection process, the IWDADL-VDC method uses the improved YOLO-v7 model. After the vehicles are detected, the next stage of classification is performed with the help of Deep Long Short-Term Memory (DLSTM) approach. In order to enhance the classification outcomes of the DLSTM model, the IWDA-based hyperparameter tuning process has been employed in this study. The experimental validation of the model was conducted using a benchmark dataset and the results attained by the IWDADL-VDC technique were promising over other recent approaches.