Enhanced Road Defect Detection with Deep Learning: Integrating Surface Texture Analysis and Background Subtraction

Maintenance of roads is necessary for the enhancement of safety standards and reduction of overall costs incurred from using a vehicle. Fusing computer-aided techniques to high-resolution photography allows for a more accurate assessment of the condition of the road. This research presents a new approach in the classification of imperfections found on a road’s surface to optimize the upkeep of the said road. It utilizes texture analysis along with the removal of the backdrop. Accroding to the research, MobileNetV2 and VGG16 net deep learning models are accurate at classifying the condition and type of surface a road has. The comparison provided some fascinating data. Transformation of maintenance procedures with the use of advanced maintenance is now possible because of how these models outperform existing methods. Claiming the use of light weight deep learning models for the preliminary assessment of road surface over standard models improves accuracy by more than five percent has proven changes in the framework and metric associated to the identification and diagnosis of issues concerning the quality of the surface to be more concentrated. This analysis will ultimately yield more effective maintenance programs. This is what will make road, infrastructure safer and more durable.