RoadAtlas: Intelligent Platform for Automated Road Defect Detection and Asset Management
This addresses the need for efficient road asset management for infrastructure professionals, but it appears incremental as it builds on existing deep learning methods.
The paper tackles the problem of expensive and time-consuming manual road inspection by developing RoadAtlas, an end-to-end integrated system for automated road defect detection and road marking parsing, which includes a web-based dashboard and backend database.
With the rapid development of intelligent detection algorithms based on deep learning, much progress has been made in automatic road defect recognition and road marking parsing. This can effectively address the issue of an expensive and time-consuming process for professional inspectors to review the street manually. Towards this goal, we present RoadAtlas, a novel end-to-end integrated system that can support 1) road defect detection, 2) road marking parsing, 3) a web-based dashboard for presenting and inputting data by users, and 4) a backend containing a well-structured database and developed APIs.