Self-training method for structural crack detection using image blending-based domain mixing and mutual learning

IF 9.6 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Quang Du Nguyen, Huu-Tai Thai, Son Dong Nguyen
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引用次数: 0

Abstract

Deep learning-based structural crack detection utilizing fully supervised methods requires laborious labeling of training data. Moreover, models trained on one dataset often experience significant performance drops when applied to others due to domain shifts prompted by diverse structures, materials, and environmental conditions. This paper addresses the issues by introducing a robust self-training domain adaptive segmentation (STDASeg) pipeline. STDASeg incorporates an image blending-based domain mixing module to minimize domain discrepancies. Additionally, STDASeg involves a two-stage self-training framework characterized by the mutual learning scheme between Convolutional Neural Networks and Transformers, effectively learning domain invariant features from the two domains. Comprehensive evaluations across three challenging cross-dataset crack detection scenarios highlight the superiority of STDASeg over traditional supervised training approaches and current state-of-the-art methods. These results confirm the stability of STDASeg, thus supporting more efficient infrastructure assessments.
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来源期刊
Automation in Construction
Automation in Construction 工程技术-工程:土木
CiteScore
19.20
自引率
16.50%
发文量
563
审稿时长
8.5 months
期刊介绍: Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities. The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.
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