Vehicle Detection and Classification via YOLOv8 and Deep Belief Network over Aerial Image Sequences

Round 1

Reviewer 1 Report

The paper discusses Vehicle Detection and Classification via YOLOv8 and Deep Belief Network over Aerial Image Sequences.

The following are the suggestions towards improvements in the paper:

1. More extensive literature review could be included.

2. Concrete gaps could be identified based on the literature which could be addressed in the paper.

3. Justification for the choice of the algorithms like Yolo8 or deep belief networks could be incorporated.

4. The need for selection of specific features can be detailed.

5. Results could be pictorially represented in the form of graphs

6. Some observations and inferences could be drawn based on the results.

7. This could lead to logical and quantifiable conclusions.

8. Comparison with existing work could be included to understand the novelty of the work.

9. The significant contribution of the work is to be detailed along with justification.

Author Response

Thanks for your valuable comments. 

Answers are added in attached files

Author Response File: Author Response.docx

Reviewer 2 Report

Comments:

I have read the manuscript. Authors have proposed a new approach for vehicle detection and classification over aerial image sequences.

·       Plagiarism is 25%. It should be below 15 %.

·       In section 2, Related works present in table form.

·       In table 5, proposed technique compared with conventional techniques. Authors have to add few more latest techniques from 2022 year and 2023 year for comparison.

·       The experimental results over the three datasets provided improved results based on classification, the proposed system achieved an accuracy of 95.6% over Vehicle Detection in Aerial Imagery (VEDAI), and 94.6 % over Vehicle Aerial Imagery from a Drone (VAID) dataset, respectively.

Authors have done good work. So, in my opinion, this manuscript may be considered for publication after revision.  

Author Response

Thanks for your valuable comments. 

Answers are available in revised file.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments to Authors Manuscript ID sustainability-2636454
Title: ”Vehicle Detection and Classification via YOLOv8 and Deep Belief Network over Aerial Image Sequences”

 Type: Article

Overview and general recommendation for authors:

The title and abstract are appropriate for the content of the text.

The authors appropriately cite past literature (a number of  46 references sources) with similar findings to theirs.

The manuscript is clear, relevant for the field and presented in a very well-structured manner.

The cited references current are mostly within the last 6 years.

The conclusions are consistent with the evidence and arguments presented.

The current article is on a topic of relevance and general interest to the readers of the journal.

 My best Regards,

The Reviewer

Author Response

Thanks for your valuable comments. 

Answers are available in revised file.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The improvements suggested have been incorporated and paper can be accepted in present form.