DA-RDD: toward domain adaptive road damage detection across different countries

Lin, C., Tian, D., Duan, X., Zhou, J., Zhao, D. and Cao, D. (2023) DA-RDD: toward domain adaptive road damage detection across different countries. IEEE Transactions on Intelligent Transportation Systems, 24(3), pp. 3091-3103. (doi: 10.1109/TITS.2022.3221067)

Abstract

Recent advances on road damage detection relies on a large amount of labeled data, whilst collecting pavement image is labor-intensive and time-consuming. Unsupervised Domain Adaptation (UDA) provides a promising solution to adapt a source domain to the target domain, however, cross-domain crack detection is still an open problem. In this paper, we propose domain adaptive road damage detection termed as DA-RDD, by incorporating image-level with instance-level feature alignment for domain-invariant representation learning in an adversarial manner. Specifically, importance weighting is introduced to evaluate the intermediate samples for image-level alignment between domains, and we aggregate RoI-wise feature with multi-scale contextual information to recover the crack details for progressive domain alignment at instance level. Additionally, a large-scale road damage dataset (based on Road Damage Dataset 2020 (RDD2020)) named as RDD2021 is constructed with 100k synthetic labeled distress images. Extensive experimental results on damage detection across different countries demonstrate the universality and superiority of DA-RDD, and empirical studies on RDD2021 further claim its effectiveness and advancement. To our best knowledge, it is the first time to investigate domain adaptative pavement crack detection, and we expect the contributions in this work would facilitate the development of generalized road damage detection in the future.

• Item Type: Articles
• Additional Information: This work was supported in part by the National Natural Science Foundation of China under Grant U20A20155, Grant 62061130221, and Grant 62173012; in part by the Natural Science Foundation of Beijing Municipality under Grant L191001; in part by the Zhuoyue Program of Beihang University (Postdoctoral Fellowship); and in part by the China Postdoctoral Science Foundation under Grant 2020M680299.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhao, Dr Dezong
• Authors: Lin, C., Tian, D., Duan, X., Zhou, J., Zhao, D., and Cao, D.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Transactions on Intelligent Transportation Systems
• Publisher: IEEE
• ISSN: 1524-9050
• ISSN (Online): 1558-0016
• Published Online: 18 November 2022
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in IEEE Transactions on Intelligent Transportation Systems 24(3): 3091-3103
• Publisher Policy: Reproduced in accordance with the publisher copyright policy