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@article{175249,
        author = {Ayan Datta},
        title = {Pothole Detection in Road Infrastructure and Developing an Early Warning System for Pothole Mitigation or Avoidance Using AI Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1883-1887},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175249},
        abstract = {Potholes are a severe challenge in road infrastructure, causing vehicle damage, increasing maintenance costs, and contributing to road accidents. Traditional pothole detection methods are manual, time-consuming, and lack efficiency. This paper explores artificial intelligence (AI)-based techniques for pothole detection and proposes an early warning system to mitigate potential hazards. A hybrid approach integrating computer vision (CNN-based deep learning models) and IoT-based vibration sensors is presented to enhance accuracy and real-time detection. Additionally, this research discusses edge computing, federated learning, and cloud-based frameworks for large-scale implementation. The proposed system demonstrates improved detection accuracy and responsiveness through experimental validation. Future enhancements, including 5G integration and self-learning AI models, are explored.},
        keywords = {Pothole detection, artificial intelligence, deep learning, IoT, road safety, early warning system, CNN, edge computing, federated learning, smart transportation.},
        month = {April},
        }