Research on Algorithm for Improving Infrared Image Defect Segmentation of Power Equipment
Round 1
Reviewer 1 Report
The authors propose a deep learning approach to improve the performance of traditional image defect segmentation algorithms. The reviewer has the following comments:
The reviewers have the following comments:
1. All the pictures should be in English, such as Figure 3
2. The clarity of the picture needs to be improved, such as Figure 6
3. Why does a "CT" appear in Figure 5?
4. This article lacks literature review and related work. The reviewer recommend the authors to read the following papers, all of which are related to deep learning:
[1] A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting
[2] Decoupled dynamic spatial-temporal graph neural network for traffic forecasting
[3] Automated surface defect detection in metals: a comparative review of object detection and semantic segmentation using deep learning
5. Limitations of the model and future plans need to be added
6. Check and correct the paper for grammatical errors
Author Response
Dear reviewer:
On behalf of all the contributing authors, I would like to express our sincere appreciations of reviewer’s constructive comments concerning our article entitled “Research on algorithm for improving infrared image defect segmentation of power equipment”(electronics-2268598). These comments are all valuable and helpful for improving our article. Based on the instructions provided in your letter, we uploaded the file of the revised manuscript. Appended to this letter is our point-by-point response to the comments raised by the reviewers.
Sincerely,
Jingwen Zhang
Author Response File: 
Author Response.docx
Reviewer 2 Report
The deep semantic features of convolutional neural networks are significant for several applications. The authors focus on the segmentation of infrared images of power equipment. Infrared imaging of power equipment is critical to fault detection, and segmentation of infrared images is an important step in power equipment fault detection. I recommend the publication of the article if the authors address the following minor changes:
1) Figure 3 labels MUST be in English.
2) Figure 6 is one of the most important figures of the article since the authors compare the performance of their algorithm with other ones and have poor quality, and the comparison should be better explained in the text.
3) The authors should compare their work to more recent literature such as:
Shu, Jun, Juncheng He, and Ling Li. "MSIS: Multispectral instance segmentation method for power equipment." Computational Intelligence and Neuroscience 2022 (2022).
Author Response
Dear reviewer:
On behalf of all the contributing authors, I would like to express our sincere appreciations of reviewer’s constructive comments concerning our article entitled “Research on algorithm for improving infrared image defect segmentation of power equipment”(electronics-2268598). These comments are all valuable and helpful for improving our article. Based on the instructions provided in your letter, we uploaded the file of the revised manuscript. Appended to this letter is our point-by-point response to the comments raised by the reviewers.
Sincerely,
Jingwen Zhang
Author Response File: Author Response.docx
Reviewer 3 Report
1. Please check the paragraph alignment of the article carefully, such as the introduction section.
2. Page 3, line 96, " __where y represents"--> "where y represents" (Please check the whole article)
3. Please convert the description in Figure 3 into English
4. Check all formats of variables in the article, for example, Page 6, line 212, "__Where Pt represents..."--> "where Pt represents..."
5. Page 7, figure 5, what's the label "CT"???
Author Response
Dear reviewer:
On behalf of all the contributing authors, I would like to express our sincere appreciations of reviewer’s constructive comments concerning our article entitled “Research on algorithm for improving infrared image defect segmentation of power equipment”(electronics-2268598). These comments are all valuable and helpful for improving our article. Based on the instructions provided in your letter, we uploaded the file of the revised manuscript. Appended to this letter is our point-by-point response to the comments raised by the reviewers.
Sincerely,
Jingwen Zhang
Author Response File: Author Response.docx
Reviewer 4 Report
Reference in conclusion should be removed.
Methodology flow chart may be included
few sentences need to be included in Introduction section
Author Response
Dear reviewer:
On behalf of all the contributing authors, I would like to express our sincere appreciations of reviewer’s constructive comments concerning our article entitled “Research on algorithm for improving infrared image defect segmentation of power equipment”(electronics-2268598). These comments are all valuable and helpful for improving our article. Based on the instructions provided in your letter, we uploaded the file of the revised manuscript. Appended to this letter is our point-by-point response to the comments raised by the reviewers.
Sincerely,
Jingwen Zhang
Author Response File: Author Response.docx
