Improving Road Safety with AI: Automated Detection of Signs and Surface Damage

Public transportation plays a crucial role in our lives, and the road network is a vital component in the implementation of smart cities. Recent advancements in AI have enabled the development of advanced monitoring systems capable of detecting anomalies in road surfaces and road signs, which can lead to serious accidents. This paper presents an innovative approach to enhance road safety through the detection and classification of traffic signs and road surface damage using advanced deep learning techniques (CNN), achieving over 90% precision and accuracy in both detection and classification of traffic signs and road surface damage. This integrated approach supports proactive maintenance strategies, improving road safety and resource allocation for the Molise region and the city of Campobasso. The resulting system, developed as part of the CTE Molise research project funded by the Italian Minister of Economic Growth (MIMIT), leverages cutting-edge technologies such as cloud computing and High-Performance Computing with GPU utilization. It serves as a valuable tool for municipalities, for the quick detection of anomalies and the prompt organization of maintenance operations.