Published Paper Details:

Because road damage has resulted in numerous deaths, research into road damage detection, particularly hazardous road damage detection and warning, is essential for traffic safety. Existing road damage detection systems mostly process data on the cloud, which has a large latency due to long-distance transmission. Meanwhile, in these systems that require big, carefully labeled datasets to achieve outstanding performance, supervised machine learning methods are typically used. We suggest using Deep Learning to detect and warn about road damage in this study. The foundation of road surface analysis is visual observations by persons and quantitative analysis by pricey tools. Visual inspection, for example, not only necessitates the use of experienced road managers but also takes time and money. Furthermore, visual inspection is inherently unreliable and inconsistent, increasing the risk. Vi- visual inspection of roads by engineers takes a long time due to the length of roads or freeways. As a result, establishing an AI-based automated system that can determine the sort of damage can help to improve and enhance the way road conditions are assessed.