Structural crack detection using deep learning–based fully convolutional networks. Issue 16 (December 2019)

• Record Type: Journal Article
• Authors: Ye, Xiao-Wei; Jin, Tao; Chen, Peng-Yu
• Other Names: Zhou Lu guest-editor.; Ni Yi-Qing guest-editor.
• Is Part Of: Advances in structural engineering. Volume 22:Issue 16(2019)
• Page Start: 3412
• Page End: 3419
• Publication Date: 2019-12
• Subjects: convolutional neural networks -- deep learning -- fully convolutional networks -- structural crack detection -- structural health monitoring; Structural engineering -- Periodicals; Construction, Technique de la; Structural engineering; Periodicals; 624.1
• DOI: 10.1177/1369433219836292
• Languages: English
• ISSNs: 1369-4332

Abstract

Cracks are a potential threat to the safety and endurance of civil infrastructures, and therefore, careful and regular structural crack inspection is needed during their long-term service periods. Many image-processing approaches have been developed for structural crack detection. However, like traditional edge detection algorithms, these methods are easily disturbed by the environmental effect. Convolutional neural networks are newly developed methods and have excellent performances in the image-classification tasks. This study proposes a fully convolutional network called Ci-Net for structural crack identification. Pixel-level labeled image training data are obtained from the online data set. Four indices are adopted to evaluate the performance of the trained Ci-Net. Crack images from an indoor concrete beam test are adopted for validation of its structural crack recognition capacity. The recognition results are also compared with those obtained by the edge detection methods. It indicates that Ci-Net exhibits a better performance over the edge detection methods in structural damage detection.