Abstract
In the present-day scenario, the government needs accurate information for effective road maintenance at regular intervals but road inspection requires enormous amounts of manpower every year and this obviously slows down the process due to the distance involved. So, detection of potholes on roads is noticeably required by the government for maintaining road which can be done by the techniques of deep learning. The main purpose of the project is to classify the images of roads based on condition/status, that is either it is a plain road or road with potholes. This model initially takes the pictures of the roads which is our dataset as input. These inputs are into the deep learning classification algorithms to classify the images of roads, and this classification can be helpful to assess road condition. This project replaces external manpower for road maintenance. This model is useful for the government for better road maintenance with less manpower in a small period of time.
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Bhavya, P., Sharmila, C., Sai Sadhvi, Y., Prasanna, C.M.L., Ganesan, V. (2021). Pothole Detection Using Deep Learning. In: Saha, S.K., Pang, P.S., Bhattacharyya, D. (eds) Smart Technologies in Data Science and Communication. Lecture Notes in Networks and Systems, vol 210. Springer, Singapore. https://doi.org/10.1007/978-981-16-1773-7_19
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DOI: https://doi.org/10.1007/978-981-16-1773-7_19
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