Crack Identification for Bridge Structures Using an Unmanned Aerial Vehicle (UAV) Incorporating Image Geometric Correction

Round 1

Reviewer 1 Report

 1.Figure 1 appears twice, can the picture in the previous article be replaced by other pictures with the same content, the GPS module in Figure 2 can not see clearly what is on the display interface, including the operation interface mentioned in the later article, can be put on a picture of the interface to give a more intuitive representation. Figure 10 is not clear enough to see the location of the cracks from the figure and thus cannot determine whether the UVA is in the correct working position.

2. The paper gives a lot of comparisons in terms of data, it is intuitive to feel the advantages of UVA can be more accurate detection of cracks; in the background introduced in the traditional detection methods for expensive disadvantages, whether UVA has economic, whether the economic analysis can be given.

 

3. The organization is slightly unclear, page 5, line 15 states that the order of the next discussion is crack image acquisition - crack image processing - crack information extraction, the first part of the content is not shown in the title, whether it is more reasonable to change the title of the third point on page 6 to image acquisition.

4．The field test only selects a single scenario, whether there will be specificity, and there may be differences in the cracking situation of different bridges.

5. For such medium to large drones, there are certain problems with range, how long a set of fully charged batteries can support the work, and how practical should be discussed.

6. The testing process is whether it will be influenced by other environmental factors.

7.In line 13 of page 3, edge detector is mentioned, but only several edge detectors are mentioned. It would be better to list and explain the principle and use method.

8.Could you give me one more example to make the conclusion more convincing.

9.The arrow pointing in the second picture of Figure 10 is not accurate

 

10."Perspective distortion" and "perspective distortion" are mentioned in the text. Could you please explain the two concepts?

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Very interesting paper, on a subject with clear practical application.

The paper is well written, with a careful description of the proposed method, and the results of its application are duly validated.

I think it will be interesting to clarify the following question: how to inspect the bottom face of the deck?  Can the lens be directed, or is it necessary to place the UAV in a vertical position?

Editorial comments:

- On page 2 (Introduction) figure 1 inadvertently appears.

- The quality of figure 10 should be improved.

- Table 1: "Crack width by width measurement" is not clear; it should be improved.

 

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 3 Report

Interesting paper, description of proposed method is clear and results prove, that method is working well. Maybe some comments to working conditions can be added to description (e.g. wind velocity, distance od UAV to bridge, opticall stabilisation, ...).

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The response well addressed my comments. 

Based on the reference, I would suggest to bring strong relevance to the scope of this journal by investigating most recent literature. Better relevant to Buildings. 

Author Response

Point 1: Based on the reference, I would suggest to bring strong relevance to the scope of this journal by investigating most recent literature. Better relevant to Buildings.

 

Response 1: Thanks for your suggestion. 5 recent references on UAV and crack identification have been added as follows.

 

[12] Li L, Chen J, Su X, Nawaz A. Advanced-Technological UAVs-Based Enhanced Reconstruction of Edges for Building Models. Buildings. 2022;12:1248.

[13] Wang D, Shu H. Accuracy Analysis of Three-Dimensional Modeling of a Multi-Level UAV without Control Points. Buildings. 2022;12:592.

[32] Ficapal A, Mutis I. Framework for the detection, diagnosis, and evaluation of thermal bridges using infrared thermography and unmanned aerial vehicles. Buildings. 2019;9:179.

[46] Wu C-S, Zhang J-Q, Qi L-L, Zhuo D-B. Defect Identification of Concrete Piles Based on Numerical Simulation and Convolutional Neural Network. Buildings. 2022;12:664.

[49] Munawar HS, Ullah F, Shahzad D, Heravi A, Qayyum S, Akram J. Civil infrastructure damage and corrosion detection: An application of machine learning. Buildings. 2022;12:156.

Author Response File: Author Response.pdf