Anomaly detection of defects on concrete structures with the convolutional autoencoder. (August 2020)

• Record Type: Journal Article
• Authors: Chow, J.K.; Su, Z.; Wu, J.; Tan, P.S.; Mao, X.; Wang, Y.H.
• Is Part Of: Advanced engineering informatics. Volume 45(2020)
• Publication Date: 2020-08
• Subjects: Anomaly detection -- Unsupervised learning -- Convolutional autoencoder -- Concrete structure -- Cracking -- Spalling; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2020.101105
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: Deep learning model is applied for the anomaly detection of concrete defects. The model training is in the unsupervised mode, with no label needed. This anomaly detection technique is adaptable to defects on wide ranges of scales. The technique outperforms classical automatic methods in concrete defect detection. Anomaly scores of the anomaly map alert inspectors for any potential defects. Abstract: This paper reports the application of deep learning for implementing the anomaly detection of defects on concrete structures, so as to facilitate the visual inspection of civil infrastructure. A convolutional autoencoder was trained as a reconstruction-based model, with the defect-free images, to rapidly and reliably detect defects from the large volume of image datasets. This training process was in the unsupervised mode, with no label needed, thereby requiring no prior knowledge and saving an enormous amount of time for label preparation. The built anomaly detector favors minimizing the reconstruction errors of defect-free images, which renders high reconstruction errors of defects, in turn, detecting the location of defects. The assessment shows that the proposed anomaly detection technique is robust and adaptable to defects on wide ranges of scales. Comparison was also made with the segmentation results produced by other automatic classical methods, revealing that the results made by the anomaly map outperform other segmentation methods, in terms of precision, recall, F1 measure and F2 measure, without severe under- and over-segmentation. Further, instead of merely being a binary map, each pixel of the anomaly map is represented by the anomaly score, which acts as a risk indicator for alerting inspectors, wherever defects on concrete structures are detected.