Hyperparameter Tuned Deep Autoencoder Model for Road Classification Model in Intelligent Transportation Systems

Abstract

Unmanned aerial vehicles (UAVs) have significant abilities for automatic detection and mapping of urban surface materials due to their high resolution. It requires a massive quantity of data to understand the ground material properties. In recent days, computer vision based approaches for intelligent transportation systems (ITS) have gained considerable interest among research communities and business people. Road classification using remote sensing images plays a vital role in urban planning. It remains challenging because of scene complexity, fluctuating road structures, and inappropriate illumination circumstances. The design of intelligent models and other machine learning (ML) approaches for road classification has yet to be further explored. In this aspect, this paper presents a metaheuristics optimization with deep autoencoder enabled road classification model (MODAE-RCM). The presented MODAE-RCM technique mainly focuses on the classification of roads into five types, namely wet, ice, rough, dry, and curvy roads. In order to accomplish this, the presented MODAE-RCM technique exploits modified fruit fly optimization (MFFO) with neural architectural search network (NASNet) for feature extraction. In order to classify roads, an interactive search algorithm (ISA) with a DAE model is used. The exploitation of metaheuristic hyperparameter optimizers helps to improve the classification results. The experimental validation of the MODAE-RCM technique was tested by employing a dataset comprising five road types. The simulation analysis highlighted the superior outcomes of the MODAE-RCM approach to other existing techniques.