AI Enabled Road Health Monitoring System for Smart Cities.

EasyChair Preprint 9151

Abstract

Automatic crack identification is a difficult issue, that has been explored for decades, due to the complexity of roadway networks. Any pavement that has cracks in it’s beginning to lose its surface integrity. Therefore, fracture identification and management are essential duties because cracks that spread severely damage structures. Manual inspection is limited to places that can be accessed by people and is based on the expert's prior knowledge. This paper describes the methods used for evaluation of roads on pothole and crack detection in the highway pavement. The objective of this research work is to create a precise pavement health smart monitoring system that requires a phone camera-based monitoring and with the help of profiling of whole cross section of the road. 1 km stretch area in RGIPT campus is considered for this study. In this research work road pavement is converted in 3D point cloud with the help of mobile camera. Large pavement area is divided into number of grids to study the nature of terrain and various features of roads. With the help of point cloud every grid is studied for their coordinate axes as well RGB values to identify the variation of z-coordinates as we proceed forward. We have tried to bring automation in road health monitoring using AI-ML. Identification and feature extraction is performed using classification in depth variation as well as corresponding change in RGB value of generated point cloud. Obtained results are very accurate with the help of image processing, and results are also verified with actual pavement surface.

Keyphrases: Artificial Intelligence, Road Health Monitoring, image processing, point cloud, pothole detection

@booklet{EasyChair:9151,
  author    = {Arpit Kumar Bhatt and Susham Biswas},
  title     = {AI Enabled Road Health Monitoring System for Smart Cities.},
  howpublished = {EasyChair Preprint 9151},
  year      = {EasyChair, 2022}}