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@article{191454,
        author = {VISHWAA T and PRASANNA RAJ K and MOUNESH M and SIVAMARIKANNAN M and DHIVAKAR B},
        title = {AI Powered Real Time Road Anomaly Detection Using Yolov11 and IoT Integration for Indian Smart Cities.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {6870-6875},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191454},
        abstract = {Road infrastructure plays a vital role in Indian transportation safety, efficiency and economics growth of India. However, issues such as potholes, cracks, and road encroachments pose significant risks to commuters and increase maintenance costs. Traditional road inspection methods are often slow, labour-intensive, and prone to human error. This project presents an AI-powered road safety system that provides real-time automated detection of road anomalies using advanced computer vision techniques. The system employs a YOLOv11-based model integrated with vehicle-mounted cameras to detect potholes, cracks, and encroachments in live road images. Each detected anomaly is GPS-tagged and transmitted to a central dashboard, enabling timely maintenance actions and improved road safety. The AI-powered solution aims to enhance road safety, reduce maintenance delays, and provide a scalable method for continuous monitoring of road infrastructure in the Indian cities.},
        keywords = {AI; Computer Vision; Road Safety; YOLOv11; Real-Time Detection, Smart Cities India.},
        month = {January},
        }