This paper presents a deep learning-based concrete crack detection technique using hybrid images. The hybrid images combining vision and infrared (IR) thermography images are able to improve crack detectability while minimizing false alarms. Large scale concrete-made infrastructures such as bridge, dam, and etc. can be effectively inspected by spatially scanning the hybrid imaging system including vision camera, IR camera and continuous-wave line laser. However, the decision-making for the crack identification often requires experts’ intervention. As a target concrete structure gets larger, automated decision-making becomes more necessary in the practical point of view. The proposed technique is able to achieve automated crack identification by modifying a well-trained convolutional neural network using a set of crack images as a training image set, while retaining the advantages of hybrid images. The proposed technique is experimentally validated using a lab-scale concrete specimen developed with various-size cracks. The test results reveal that macro- and micro-cracks are automatically detected with minimizing false-alarms.
Yun-Kyu An, Keunyoung Jang, Byunghyun Kim, and Soojin Cho, "Deep learning-based concrete crack detection using hybrid images," Proc. SPIE 10598, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, 1059812 (Presented at SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring: March 06, 2018; Published: 27 March 2018); https://doi.org/10.1117/12.2294959.
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