IoT-Based Pothole Detection and Analysis for Road Quality Assessment and Human Safety Using Machine Learning Approach

One of the significant problems faced by developing countries is the maintenance of road conditions. Road infrastructure for the society is very important because the majority of road accidents take place due to bad condition of roads like potholes. Potholes occur due to poor quality of construction and badly maintained roads. We propose to develop an IoT-based system to detect and analyse various potholes for monitoring the road condition and presenting the data in an organized way using an efficient machine learning approach named Support Vector Machine (SVM) in a novel manner. The proposed SVM proved to be a better accuracy model than approaches. This system also handles some parameters like Pothole Depth, Latitude and Longitude effectively. This system will help the government or the regulatory bodies to analyse the roads for further work. This proposed system consists of ultrasonic sensors, gyroscope, GSM and GPS module. This proposed model will be attached to vehicles and the data obtained from sensors will be sent to the ThingSpeak Cloud Platform. We also aim to develop a Flutter-based application to present the data gathered in the cloud. This includes plotting the exact location of potholes on a map using Leaflet and MapBox API to give a more efficient way of identifying danger zones in terms of road condition. Using this data, more damaged areas can be prioritized and damage control can be reduced.