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

被引:6
|
作者
Zhou, Haiyan [1 ]
Liu, Ying [1 ]
Liu, Zheng [1 ]
Zhuang, Zilong [1 ]
Wang, Xu [1 ]
Gou, Binli [1 ]
机构
[1] Nanjing Forestry Univ, Coll Mech & Elect Engn, Jiangsu Coinnovat Ctr Efficient Proc & Utilizat Fo, Nanjing 210037, Peoples R China
来源
FORESTS | 2022年 / 13卷 / 11期
基金
中国国家自然科学基金;
关键词
engineered bamboo; digital image correlation; super-resolution reconstruction; crack detection; generative adversarial network; SUPER RESOLUTION RECONSTRUCTION; IMAGE SUPERRESOLUTION; NEURAL-NETWORK; ALGORITHM; ENHANCEMENT; SRGAN;
D O I
10.3390/f13111896
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
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%.
引用
收藏
页数:13
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