ORCNN-X: Attention-Driven Multiscale Network for Detecting Small Objects in Complex Aerial Scenes

Round 1

Reviewer 1 Report

This manuscript proposes a new framework for small oriented object detection in remote sensing images. The whole framework consists of a feature extraction module, a feature pyramid module, Oriented RPN (ORPN), and a prediction head. The evaluation of the method presented in the manuscript on two public benchmark datasets, DOTA and HRSC2016, demonstrates its state-of-the-art performance. However, the manuscript contains a number of errors and needs to be carefully revised.

(1) Figure 1. Flowchart of the proposed method is not clear enough to see the four parts included in the proposed method, while the drawing of W-PAFPN can cause misunderstanding.

(2) The use of the two 'W's in Equation 1 causes confusion.

(3) Should k-1 in the denominator of Equation 4 be k = 1?

(4) The value in line 311 of the manuscript (81.27%) is different from the value in the corresponding table (52.30%) and needs to be verified.

(5) The evaluated metrics corresponding to the value in Table 3 needs to be clarified. In the textual explanation section, it is stated that the corresponding metrics is mAP, and this also needs to be verified.

In addition, the following changes are suggested in the manuscript:

(1) The abbreviated proposal in the manuscript corresponds to the proposed method, not to the method on which it is based.

(2) The comparison in Figure 5 is more appropriate for ResNeSt101 and ResNeSt+101 networks.

This manuscript proposes a new framework for small oriented object detection in remote sensing images. The whole framework consists of a feature extraction module, a feature pyramid module, Oriented RPN (ORPN), and a prediction head. The evaluation of the method presented in the manuscript on two public benchmark datasets, DOTA and HRSC2016, demonstrates its state-of-the-art performance. However, the manuscript contains a number of errors and needs to be carefully revised.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

Dear Authors,

1.      The proposed approach of utilizing ORCNN-X for detecting small objects in aerial scenes shows promise in addressing the challenges posed by variations in resolutions, aspect ratios, dimensions, and noise commonly encountered in aerial object detection.

2.      Abbreviations: It is recommended to expand any abbreviations used in the manuscript upon their first mention to enhance readability and clarity for readers.

3.      References: The manuscript should be revised to ensure a systematic presentation of references. The numbering of references should commence from 1, instead of starting with reference 13. This adjustment will improve the organization and flow of the manuscript.

4.      Contribution (Line 58~64): The manuscript should provide a more specific and clear delineation of its contributions. It is advisable to articulate the contributions in a more explicit manner to enhance comprehension.

5.      Related work and methodology: The manuscript should address the confusion observed between the related work section (e.g., ResNeSt, PAFPD) and the methodology section. The proposed algorithm for overcoming the limitations associated with detecting noisy/low resolution/different dimension aerial images, as outlined in the introduction, should be clearly explained without relying on external references.

6.      Dataset: The manuscript effectively utilizes publicly available DOTA and HRSC2016 datasets, providing a comprehensive explanation of their use.

7.      Results: The manuscript adeptly presents the experimental results, including a comparison between ResNet and ResNeSt+ and an improvement in mean Average Precision (mAP) scores. Furthermore, the accuracy and speed of the proposed method are compared against other state-of-the-art approaches, demonstrating superior performance.

Please note that these revisions aim to enhance the scientific rigor and clarity of the manuscript.

-

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 3 Report

The proposed ORCNN-X looks very challenging and advantageous in detecting small objects in complex aerial scenes However, I have some comments:

1)      Is there any special reason behind choosing a validation data set of 400 images while training and testing are 1200 images?

2)      The abstract and the comparison to other works needs to be reviewed at some point to better appeal the advantage of the proposed method.

English proofreading might be required to address some typos and grammar error.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 4 Report

1.What is the main question addressed by the research?

The paper presents a new method for finding objects in remote sensing images. Methodology: feature extraction procedures are created, oriented objects are worked with, the feature pyramid module that  includes four branches that handle featuresand the authors present action prediction. Two public databases (DOTA and HRSC2016) were used on which the authors tested the proposed method.

2. Do you think the topic is original or relevant to the field?

According to the reviewer, the authors' study has elements of originality and is suitable for publication in the journal.

3. What does it add to the subject area compared with other published

material?

The proposed method generates anchors with predefined angles instead of horizontal anchors to cover the possible orientation range of objects, which the authors say helps to accurately detect objects with bounded boxes.

Recommendation: the authors can describe the advantages of their method over already published similar studies in this area.

4. What specific improvements should the authors consider regarding the

methodology? What further controls should be considered?

According to the reviewer, the presented figure 1 is very schematic and one cannot get a concrete idea of the proposed method from it.

Suggestion: describe the four procedures referred to in the proposed method more clearly and more thoroughly, so that they become easily understandable for readers.

Recommendation: each of the four parts of the proposed method should be described with a separate diagram and a detailed description should be attached to each diagram.

5. Are the conclusions consistent with the evidence and arguments presented

and do they address the main question posed?

Sufficient results are presented to support the study and the conclusions drawn are supported by the results obtained.

6. Are the references appropriate?

Yes.

7. Please include any additional comments on the tables and figures.

The description of figure 2 should come before the figure itself, not after it.

The individual blocks of the diagrams presented in the figure 2 should be described in detail. To present a comparison of different indicators of the two schemes. Which of the two schemes is better and according to what indicators.

The parameters in the equations should be explained. Different parameters should not be given the same notation. Equation 2 should be checked for correctness against the parameters used.

In equation 4, a syntax error was made when using the parameter k.

*In addition, please see the attached file.*

Comments for author File: Comments.pdf

 Minor editing of English language required.

Author Response

Please see the attachment.

Author Response File: Author Response.docx