A Transfer Learning Approach for Enhancing Road Surface Classification in Intelligent Driving Systems

Road surface classification is very critical in the domain of intelligent driving systems as it ensures enhanced safety and comfort in autonomous vehicles. In this paper, a transfer learning approach is proposed for effectively classifying the road surface with enhanced accuracy and real-time performance mitigating the constraints of traditional image processing techniques A deep learning model is developed and trained in this research using an open-source dataset, encompassing 72,400 images capturing distinct road surface characteristics present across varying terrains. This research adopts pre-trained models like Mobilenet, VGG-19 and Resnet101 as feature extractors to optimize and improve the classification performance of the deep learning model significantly. The proposed methodology is evaluated using different key performance metrics such as loss, accuracy, average rate of improvement and change in accuracy, and total training time. The proposed deep learning technique achieved an accuracy of $\mathbf{9 8. 7 7 \%}$, signifying its critical importance for real-world applications and practical deployment.