Performance Evaluation of Several Transfer Learning Models for Classification of Road Surface State

Abstract

Using a customized approach, automatic classification of road surface condition and categorized data storing are proposed using DenseNet201. Road surface distress is one of the main issues affecting transportation safety. The first indication of a catastrophic asphalt pavement collapsing is a surface crack, that can later develop into a pothole and result in high repair costs. By replacing the surveillance system with the automated software program that we are recommending in this analysis, the traditional methods for identifying cracks or degradation in a road's surface, which involved manual examination by people, can be eliminated. DenseNet201 has outperformed other compared models with an accuracy of 98.75% and the most minimal model loss while testing while classifying damaged and smooth road surfaces. Later, certain governing bodies responsible for preserving the quality of road infrastructure can use the model's classified images.