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Crack Detection Method for Engineered Bamboo Based on Super-Resolution Reconstruction and Generative Adversarial Network

Authors :
Haiyan Zhou
Ying Liu
Zheng Liu
Zilong Zhuang
Xu Wang
Binli Gou
Source :
Forests; Volume 13; Issue 11; Pages: 1896
Publication Year :
2022
Publisher :
Multidisciplinary Digital Publishing Institute, 2022.

Abstract

Engineering bamboo is a type of cheap and good-quality, easy-to-process material, which is widely used in construction engineering, bridge engineering, water conservancy engineering and other fields; however, crack defects lead to reduced reliability of the engineered bamboo. Accurate identification of the crack tip position and crack propagation length can improve the reliability of the engineered bamboo. Digital image correlation technology and high-quality images have been used to measure the crack tip damage zone of engineered bamboo, but the improvement of image quality with more-advanced optical equipment is limited. In this paper, we studied an application based on deep learning providing a super-resolution reconstruction method in the field of engineered bamboo DIC technology. The attention-dense residual and generative adversarial network (ADRAGAN) model was trained using a comprehensive loss function, where network interpolation was used to balance the network parameters to suppress artifacts. Compared with the super resolution generative adversarial network (SRGAN),super resolution ResNet (SRResNet), and bicubic B-spline interpolation, the superiority of the ADRAGAN network in super-resolution reconstruction of engineered bamboo speckle images was verified through assessment of both objective evaluation indices (PSNR and SSIM) and a subjective evaluation index (MOS). Finally, the images generated by each algorithm were imported into the DIC analysis software, and the crack propagation length was calculated and compared. The obtained results indicate that the proposed ADRAGAN method can reconstruct engineered bamboo speckle images with high quality, obtaining a crack detection accuracy of 99.65%.

Details

Language :
English
ISSN :
19994907
Database :
OpenAIRE
Journal :
Forests; Volume 13; Issue 11; Pages: 1896
Accession number :
edsair.doi.dedup.....058488b03351ee4f53919136f8138565
Full Text :
https://doi.org/10.3390/f13111896