Road Maintenance System using YOLOV7

Road transport is one of the most important types of transportation; it plays an important part in a country's economic growth by connecting communities and businesses, as well as giving access to education, job, social, and health services. Many factors, including weather, significant traffic volumes, large numbers of heavy vehicles, shoddy building methods and materials, and poor road maintenance, can cause damage to roads. Potholes are the most bothersome road damage/defect, causing discomfort to travelers, car damage, and accidents. Even if there aren't many big trucks in the traffic stream, their influence is noticeable. Due to their physical qualities of length, size, and acceleration/deceleration, heavy trucks have an adverse physical and psychological impact on the flow of nearby traffic. Potholes are frequently associated with accidents, and such data analysis from a basic set of variables to a broader set of variables will be beneficial to the Mumbai public in a variety of ways. This paper aims to limit the occurrence of potholes by tracking the number of vehicles plying over a certain road, classifying the vehicles into light transport vehicles and heavy transport vehicles and estimating the amount of load being borne by the road due to the heavy transport vehicles. Potholes are detected using YoloV7and then generalized to the entire city using an Android app and Google Maps.