Abstract

Cracks are one of the most common types of surface defects that occur on various engineering infrastructures. Visual-based crack detection is a challenging step due to the variation of size, shape, and appearance of cracks. Existing convolutional neural network (CNN)-based crack detection networks, typically using encoder-decoder architectures, may suffer from loss of spatial resolution in the high-to-low and low-to-high resolution processes, affecting the accuracy of prediction. Therefore, we propose ${{\rm HRNet}^{\rm e}}$, an enhanced version of a high-resolution network (HRNet), by removing the downsampling operation in the initial stage, reducing the number of high-resolution representation layers, using dilated convolution, and introducing hierarchical feature integration. Experiments show that the proposed ${{\rm HRNet}^{\rm e}}$ with relatively few parameters can achieve more accuracy and robust performance than other recent approaches.

© 2021 Optical Society of America

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Data underlying the results and source code presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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