A Real-Time Road Surface Identification and Tracking Based on Raspberry PI Assisted IoT Enabled Image Processing Scheme

These days, an increasing number of individuals are harmed in automobile accidents that are brought on by roads that are not properly maintained. The situation is especially prevalent in countries such as India. When it rains, the roads become slippery, and potholes consume a significant portion of the road surface. This results in a wide range of exterior automobile problems, such as flat tyres, decreased body damage, accidents, and other issues. In the case that potholes are discovered, drivers must to be informed so that they can take appropriate action. While it is feasible to send an alarm message to the drivers, it is also possible to record their whereabouts. The process of identifying potholes is an essential step in the process of carefully planning and rebuilding broken pavement. Traditional equipment and activities that vary in depth served as the basis for the ways that are currently being used for estimating and detecting potholes. In order to address these issues, we work to develop a lightweight system that does not considerably impair the accuracy of identification. In order to construct the hardware for the system, Raspberry PI is utilized. Cross-validation is performed on the new model in order to evaluate its performance in comparison to an existing system that use an Arduino-UNO controller. This system is known as the Arduino-based Road Surface Detector (ARSD). The Raspberry PI Road Surface Detector (RRSD) is the specific name given to this particular system. Reimagining the item identification system is accomplished through the utilization of a basic hardware technique that is based on Raspberry Pi. Object identification for a vast region in both two-dimensional and three-dimensional perspectives is finally achievable with an easy setup and a hardware connection. This is a groundbreaking development. Object detection has rapidly emerged as one of the most fascinating subfields within the field of computer vision, and over the past few years, a large number of academics have been exerting a great deal of effort in order to create the most effective model for object identification.