Final Year Project

Project Title

Road Damage Detection Using Convolutional Neural Networks with Real-time Web Application Monitoring (Crack Track)

Description

This project is about the automation of the road inspection and road health monitoring mechanism, procedures, and protocols. How might we improve the road damage and health monitoring mechanism by using an integrated real-time one-window solution which allows automated health monitoring of roads for relevant authorities responsible for monitoring and repairing of roads. So that they have a faster, cheaper, safer, less labor-intensive and a reliable process that can be used to monitor road conditions and repair roads as early as possible. This project aims to improve the current manual process, and protocols of inspection of the roads, which is unreliable and inefficient. Road Damage Detector, i.e. Crack-Track, is a single-unit integrated solution for maintaining a real-time database of road conditions with live location tracking of the detector and automating the manual inspection process for relevant authorities. For example, KMC - Karachi Metropolitan Corporation, DMC - District Municipal Corporation, DHA - Defence Housing Authority, Malir Cantonment, Bahria Town, other private contractors/builders or authorities who are mainly responsible for road maintenance and repair in Karachi.