Journal
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
Volume 18, Issue 5-6, Pages 1722-1737Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/1475921718821719
Keywords
Concrete crack detection; deep convolutional neural network; hybrid image scanning; vision image; infrared thermography; structural health monitoring
Funding
- Construction Technology Research Program - Ministry of Land, Infrastructure and Transport of Korean government [17SCIP-C116873-02]
- Basic Science Research Program of the National Research Foundation of Korea (NRF) - Ministry of Science, ICT and Future Planning [2015R1C1A1A01052625]
- National Research Foundation of Korea [2015R1C1A1A01052625] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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This article proposes a deep learning-based autonomous concrete crack detection technique using hybrid images. The hybrid images combining vision and infrared thermography images are able to improve crack detectability while minimizing false alarms. In particular, large-scale concrete-made infrastructures such as bridge and dam can be effectively inspected by spatially scanning the unmanned vehicle-mounted hybrid imaging system including a vision camera, an infrared camera, and a continuous-wave line laser. However, the expert-dependent decision-making for crack identification which has been widely used in industrial fields is often cumbersome, time-consuming, and unreliable. As a target concrete structure gets larger, automated decision-making becomes more desirable from the practical point of view. The proposed technique is able to achieve automated crack identification and visualization by transfer learning of a well-trained deep convolutional neural network, that is, GoogLeNet, while retaining the advantages of the hybrid images. The proposed technique is experimentally validated using a lab-scale concrete specimen with cracks of various sizes. The test results reveal that macro- and microcracks are automatically visualized while minimizing false alarms.
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