Concrete crack segmentation based on convolution–deconvolution feature fusion with holistically nested networks. Issue 8 (23rd March 2022)

• Record Type: Journal Article
• Authors: Xu, Shengjun; Hao, Ming; Liu, Guanghui; Meng, Yuebo; Han, Jiuqiang; Shi, Ya
• Is Part Of: Structural control and health monitoring. Volume 29:Issue 8(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-23
• Subjects: channel attention mechanism -- convolution–deconvolution feature fusion -- crack segmentation -- holistically nested networks -- VGG‐16 network; Structural engineering -- Periodicals; Structural control (Engineering) -- Periodicals; Automatic data collection systems -- Periodicals; Detectors -- Periodicals; 624.17
• DOI: 10.1002/stc.2965
• Languages: English
• ISSNs: 1545-2255

Abstract

Summary: Automatic crack detection on concrete surfaces has become increasingly important for the health diagnosis of concrete structures to prevent possible malfunctions or accidents. In this paper, a concrete crack segmentation network based on convolution–deconvolution feature fusion with holistically nested networks is proposed. The proposed network adopts an encoder–decoder structure and uses VGG‐16 as the basic feature extraction network. First, considering the problem that the VGG‐16 network can extract redundant features in the encoding stage, based on the channel attention mechanism, the channel spatial correlation and global information are used to emphasize crack features to remove redundant features. Second, through the convolution–deconvolution feature fusion module, the deep semantic information of the deconvolution is effectively fused with the shallow features of convolution, which effectively improves the semantic crack feature information extracted at each stage of the VGG‐16 network. Finally, based on a multiscale supervised learning mechanism, holistically nested networks are used to fuse the prediction results from different scales, which enhances the network's ability to express linear topological structures and improves the accuracy of crack segmentation. Through a large number of experiments on the Bridge_Crack_Image_Data dataset and CFD dataset, we demonstrate that compared with other deep networks, the proposed network not only achieves better segmentation results for cracks of different widths but is also more robust.