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

Round 1

Reviewer 1 Report

Review of the manuscript ID – forests-2013991

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

The manuscript submitted for review concerns the feasibility of using digital image correlation technology to identify and measure the damage zone of an engineered bamboo speckle crack using advanced optical equipment. In the paper, the Authors proposed the application of a super-resolution reconstruction method for engineered bamboo speckle images based on the ADRAGAN network. The effectiveness of the proposed algorithm was evaluated using PSNR, SSIM and MOS indices. Based on the analysis, the Authors concluded that the proposed ADRAGAN method is an effective method for evaluating cracks and their propagation of engineered bamboo, because the images reconstructed by ADRAGAN show more details that are more visible to the human eye than traditional methods.  This gives greater accuracy in detecting damage in the structural material resulting in a better assessment of the strength and stability of the building construction.

In principle, the work raises no major objections. It is of an applied character. However, several remarks arise, which he suggests to include in the content.

1. In the Introduction, the Authors presented the application possibilities of super-resolution reconstruction of image details in various fields. However, the work mainly focuses on structural lignocellulosic material. Hence, the question arises whether such a method is or has already been applied to such materials by other authors (including wood or wood-based materials) or is it a complete novelty? If so, please provide examples.

2. In the Materials and Methods subsection, the Authors have comprehensively described the equipment used and its basic parameters.  However, there is some insufficiency is the characterization of bamboo itself. Please complete this by giving, for example, the species of bamboo, its moisture content, how and where it was obtained. I would also suggest stating what number of samples were used in the study (was it 6 as in the photograph in Fig. 2, or more?).

3.   The work needs editorial correction, i.e. missing spaces in several places, rewording the test in lines 248-251, and others.

 Recommendations:

The manuscript, except for minor comments, does not raise major objections, so this suggested changes can be forwarded for further editorial process.

Accept after minor revision.

Author Response

Comment 1: In the Introduction, the Authors presented the application possibilities of super-resolution reconstruction of image details in various fields. However, the work mainly focuses on structural lignocellulosic material. Hence, the question arises whether such a method is or has already been applied to such materials by other authors (including wood or wood-based materials) or is it a complete novelty? If so, please provide examples.

Response: In addition to our team, few people have studied the use of deep learning models for super-resolution reconstruction in the field of engineering bamboo speckle image DIC.

Comment 2: In the Materials and Methods subsection, the Authors have comprehensively described the equipment used and its basic parameters.  However, there is some insufficiency is the characterization of bamboo itself. Please complete this by giving, for example, the species of bamboo, its moisture content, how and where it was obtained. I would also suggest stating what number of samples were used in the study (was it 6 as in the photograph in Fig. 2, or more?).

Response: We have added the raw materials and parameters for making engineering bamboo in line 104-110.

Comment 3: The work needs editorial correction, i.e. missing spaces in several places, rewording the test in lines 248-251, and others.

Response: We have modified as required.

Reviewer 2 Report

An article entitled „Crack Detection Method for Engineered Bamboo Based on Super-resolution Reconstruction and Generative Adversarial Network” presents an interesting study on using an application based on deep learning to provide a super-resolution reconstruction method helpful in the assessment of engineered bamboo quality regarding cracks presence. The study is important from a practical perspective and can be useful for the broader application of various wood-based products by helping to properly assess their mechanical properties. Only some minor corrections are suggested, and the comments can be found in the attached pdf file.

Comments for author File: Comments.pdf

Author Response

Comment 1: Please add a full name to these two acronyms.

Response: We have added the full name.

Comment 2: Please specify how long is it and provide a suitable literature reference.

Response: We have specified how long is it and provide a suitable literature reference.

Comment 3: The same as where?

Response: We means all the experiments were performed in the same platform, and we have clarified in the text in line 261.