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@article{165376,
        author = {Dhruvkumar Busa and Dr. Shafi Pathan},
        title = {Advanced Autonomous System for Real-Time Pothole Detection and Road Surface Monitoring Using Cutting-Edge Sensor Technology.},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {11},
        number = {1},
        pages = {873-883},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=165376},
        abstract = {Scientists are working on ways to spot potholes on streets to help with actual or offline control of vehicles (like for self-driving cars) or to collect information offline for fixing roads. For all these reasons, experts all over the globe are exploring different methods to find potholes on roads. Roads contribute substantially to the economic system and serve as a framework for mass transit. Road potholes are a key source of concern in the transport networks. Deep - learning techniques, which deals with various of image analysis and object detection techniques, Many studies suggest using automation to find potholes effectively. The method to spot potholes should work automatically, with high accuracy and trustworthiness. Potholes on roadways pose significant challenges for vehicular applications and road repair initiatives worldwide. Detecting these potholes accurately and efficiently is essential for enhancing road safety and maintaining transportation infrastructure. In this context, various approaches have been explored by researchers to develop reliable pothole detection systems. This paper reviews the state-of-the-art methodologies and technologies employed in pothole detection, focusing on the integration of deep learning techniques with image analysis and object detection methods. The economic significance of roads as a vital component of mass transit underscores the importance of addressing pothole-related concerns. Potholes not only compromise road safety but also impose financial burdens on transportation networks. Consequently, there is a pressing need for automated pothole detection systems that offer optimal precision and reliability. Deep learning techniques have emerged as promising solutions for automating pothole detection processes. By leveraging convolutional neural networks (CNNs) and other advanced algorithms, these techniques enable the extraction of intricate features from road images, facilitating accurate identification of potholes. Furthermore, deep learning-based approaches offer scalability and adaptability to diverse road conditions and environments. This paper discusses the challenges and opportunities associated with implementing deep learning-based pothole detection systems. It},
        keywords = {Deep Learning, Convolutional Neural Network, OpenCV, Potholes. Conversion: Main Points: - Deep Learning - Convolutional Neural Network (CNN) - OpenCV - Potholes We're talking about: - The smart tech of Deep Learning - How CNN helps computers see like us - Using OpenCV for image tasks - Spotting potholes with this tech},
        month = {},
        }