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Article
Peer-Review Record

YOLOv7-CHS: An Emerging Model for Underwater Object Detection

J. Mar. Sci. Eng. 2023, 11(10), 1949; https://doi.org/10.3390/jmse11101949
by Liang Zhao 1,*, Qing Yun 1, Fucai Yuan 2, Xu Ren 1, Junwei Jin 3 and Xianchao Zhu 3
Reviewer 1: Anonymous
Reviewer 2: Anonymous
J. Mar. Sci. Eng. 2023, 11(10), 1949; https://doi.org/10.3390/jmse11101949
Submission received: 8 September 2023 / Revised: 22 September 2023 / Accepted: 6 October 2023 / Published: 9 October 2023

Round 1

Reviewer 1 Report

The article is well written and presents an interesting  comparative analysis of various image enhancement algorithms to assess their effect on target detection accuracy.

The minimum hardware requirements for achieving a real-time detection speed of 32 frames per second (FPS) should be added.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

Small object detection problem was not addressed in THIS paper. Consider mentioning the further works related to this problem

Refer to the specific algorithms when you write "more precise" - more than what?

consider moving the subsection 3.1 or part of it to Section 2 - related works. it is not clear if you are using image enhancement for this work results, so mention the methods you are consireding to use

please refere to SGD and Adam Optimizer in lines 387 and 388

why is YOLOv7-tiny not used as comparison in section 4.6 as it is most suitable for detecting smaller objects?

 

 

some minor changes are necessary, like detection mechanism (algorithm?) or diverse ground images

please write the whole word equation in line 326 rather than just Equ

"breathtaking" in line 363 is not relevant to a scientific article

 

Author Response

Please see the attachment.

Author Response File: Author Response.docx

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