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arXiv 2109.033852021-09-09cs.CVcs.AIcs.LGcs.MM

RoadAtlas: Intelligent Platform for Automated Road Defect Detection and Asset Management

The University of Queensland（昆士兰大学）；Logan City Council（洛根市议会）

Zhuoxiao Chen, Yiyun Zhang, Yadan Luo, Zijian Wang, Jinjiang Zhong, Anthony Southon

备注：: Demonstration slides attached. To view attachments, please download the file listed under "Ancillary files"

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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.

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