AI-powered road damage detection for enhanced safety and life protection

Road condition assessment is one of the leading concerns for the sustainability of road safety and condition, and hence has a direct effect on transportation efficiency, particularly for older infrastructures. The road diseases of the traditional roads are manual detection and recognition. This method is slow, has high costs, and is subject to personal subjective effect factors. In this paper, we propose employing the Vision Transformer (ViT) with lightweight CNN encoders for damage detection. It was prepared and tested on a dataset of 10,000 road images. The objects in the scene may also change smoothly from one to another, so we use the Vision Transformer because it can be a more powerful model to capture the global dependencies, as well as more complex attributes of the scene, and also the images (where, with high probability, precise classification is demanded). An experiment in the research study demonstrated that the new model could be used to detect road damage with an accuracy of 94%. An automatic evaluation can be carried out hundreds of times faster than a manual evaluation and is infinitely more reliable than manual scoring. AI-driven infrastructure monitoring that supports vehicle safety, making them serviced on time and reducing operational costs. The model can also be applied to various potential applications, including the development of a more effective road management system and the financing of maintenance, conservation, and road network expansion.