• M.Saritha Devi
• G.Devi Bhavani
• A.Chitti Babu
• D.Venkata Sekhar
• T.Hima Swetha

An essential but unfinished challenge is the creation of a surface recognition system for automobiles. In this research, we take a fresh look at surface categorization by examining photographs of actual road surfaces. High surface classification accuracy is achieved by using the suggested experimental approach in conjunction with a deep neural network. This study contains original work for road surface categorization using Deep Learning architecture. The importance of studying the vehicle's status has grown as technologies like the advanced driver assistance system (ADAS) and autonomous driving have progressed. However, studies on how to categorize road surfaces have not been done as of yet. If the control system is capable of classifying and recognizing road surfaces, it can verify the data from other sensors and make a more confident decision. That's why it's so important to categorize the road surfaces.

Deep learning architecture, Advanced driver assistance(ADAS), Autonomous driving technologies.

"ROAD SURFACE CLASSIFICATION USING RESNET", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 2, page no.e438-e444, February-2023, Available :http://www.jetir.org/papers/JETIR2302449.pdf

"ROAD SURFACE CLASSIFICATION USING RESNET", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 2, page no. ppe438-e444, February-2023, Available at : http://www.jetir.org/papers/JETIR2302449.pdf