期刊
AUTOMATION IN CONSTRUCTION
卷 118, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.autcon.2020.103291
关键词
Computer vision; Damage detection; Deep learning; Defect detection; Distance transform; Faster R-CNN
资金
- NSERC [RGPIN-2016-05923, 533690, EGP/515025]
- Research Manitoba 2018 New Investigator Operating Grant [3481]
This paper proposes an automatic crack detection, localization, and quantification method using the integration of a faster region proposal convolutional neural network (Faster R-CNN) algorithm to detect crack regions. The regions were located using various bounding boxes and a modified tubularity flow field (TuFF) algorithm to segment the crack pixels from the detected crack regions. A modified distance transform method (DTM) was used to measure crack thickness and length in terms of pixel measurement. To validate the proposed method, 100 images were taken in different places with complex backgrounds containing different angles and distances between the camera and the objects. The results obtained from the Faster-R-CNN-based crack damage detection had a 95% average precision. The pixel-level segmentation performance of the modified TuFF algorithm exhibited an authentic outcome, with 83% intersection over union. Finally, the modified DTM algorithm provided 93% accuracy with respect to crack length and thickness with a 2.6 pixel root mean square error.
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