首页|New Machine Learning Study Results from School of Computer Science and Engineeri ng Described (Pavement Distress Detection, Classification, and Analysis Using Ma chine Learning Algorithms: A Survey)
New Machine Learning Study Results from School of Computer Science and Engineeri ng Described (Pavement Distress Detection, Classification, and Analysis Using Ma chine Learning Algorithms: A Survey)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on artificial intelligence have been published. According to news reporting originating from Tamil Nadu, In dia, by NewsRx correspondents, research stated, "Distress is any observable dete rioration or damage that negatively impacts the road's performance and safety." Funders for this research include Manipal Academy of Higher Education (Mahe), Ma nipal, Karnataka, India. The news journalists obtained a quote from the research from School of Computer Science and Engineering: "Potholes cracks, rutting, and bleeding are a few examp les of distress. Maintaining the roads and detecting distress on the surface of the road is critical to avoid impending accidents, consequently saving lives. Th e article primarily explains the systematic approach of autonomous techniques fo r detecting distress such as potholes and cracks. Among the array of methods emp loyed for finding distress, the current study reviews the features of three diff erent artificial intelligence (AI) techniques, which include machine and deep le arning approaches. Applications of these techniques help in finding pavement dis tress apart from the vibration, 2D, and 3D methods. This systematic approach exp lains the autonomous techniques for detecting surface distress, the scope of com bining those approaches, and their limitations."
School of Computer Science and Engineeri ngTamil NaduIndiaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine Learning
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