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@article{159507,
        author = {Pranav Prashant Kulkarni and Ketan Mandar Kulkarni and Vivek Rajkumar Nimbalkar and Prof S.P Jadhav},
        title = {Android Pothole Detection System Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {12},
        pages = {103-107},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=159507},
        abstract = {This abstract discusses the challenges of maintaining road networks despite recent advancements in transportation technology. Potholes and other road defects are a common problem that can lead to accidents, traffic congestion, and expensive repairs. To address this issue, a proposed android pothole detection system that utilizes smartphone sensors and machine learning algorithms is introduced. The system can detect potholes using a smartphone camera and distinguish them from other road irregularities. It can also store and analyze data on road conditions, enabling authorities to prioritize maintenance and repairs. The proposed solution aims to crowdsource information from users and send it to relevant authorities using an Android application. The success of this solution depends on several factors, including the accuracy of the Deep Learning model, the quality of user-provided information, and the responsiveness of relevant authorities. The proposed android pothole detection system can offer several benefits, including reducing road accidents, lowering repair costs, and minimizing traffic congestion. Overall, utilizing technology for android pothole detection has the potential to revolutionize road maintenance and safety, providing safer and more efficient means of travel for everyone.},
        keywords = {android, crowdsource, Deep Learning, machine learning algorithms, pothole detection system, road maintenance, road safety, smartphone sensors.},
        month = {},
        }