An Improved YOLOv5s-Based Scheme for Target Detection in a Complex Underwater Environment

Round 1

Reviewer 1 Report

The manuscript entitled" Research on underwater complex environment target detection based on improved YOLOv5s" presents very solid scientific and technical content. The authors have excellently represented the experimental basis of the work done. The various paragraphs were treated in a straightforward and clear manner. It is requested to revise to the formatting of the figures and tables based on the guidance provided for the authors. The manuscript in its entirety can be accepted in the form in which it was submitted.

English requires minor revisions

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

This manuscript discusses an algorithm for sea cucumbers and sea urchins as target detection in complex underwater environments, The study proposes an improved YOLOv5s which plays a crucial role in the seafood aquaculture industry. The research topic is interesting and appealing research community, Moreover, the topic of this manuscript falls within the scope of the Journal of Marine Science and Engineering.

However, before it is considered for publication, some issues should be addressed as follows:

1.     The title needs revision, such as, “An improved YOLOv5s based scheme for target detection in a complex underwater environment”.

2.     Overall English grammar and composition/syntax/sentence structure needs modification for better understanding.

3.     Basic equations such as 13,14 need elimination from the revised manuscript, similarly few formulas are listed in this study. Are those formulas developed in this study? If not, please add their citation

4.     Is there any weight file prior to training the model? If yes, where does the file come from, and how were those files established?

The number of references in the paper is insufficient, it is suggested to add a few related citations.

Overall English grammar and composition/syntax/sentence structure needs modification for better understanding.

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

The topic & way the authors presented the work is really good, but here i've few suggestions to the authors to improve the overall quality of the manuscript.

1) The description of the database is too simple & provide the information of image annotation.

2) The paper does not present any novelty; it presents a practical application of an existing deep learning model for under water object detection. The study could benefit from additional experimentation with different datasets and more models to increase the generalizability of the findings. 

3) Why only SSD is compared with YOLO models, Why not other? give justification in your manuscript.

4) the authors need to provide a comprehensive explanation of how each model was implemented, the paper does not provide a clear description of the dataset used for training and testing, which is crucial information for the readers to evaluate the validity of the results. 

5) Figure-7, Kindly provide ROC curves for Precision, Recall & [email protected]. The ROC curves must demonstarte all implemented models so that readers can validate your results.

Spell & grammer checks required.

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Thanks to Authors for addressing my suggestions, if possible add this recent research work on various Yolo Models in your reference section:

Kumar, N.; Nagarathna; Flammini, F. YOLO-Based Light-Weight Deep Learning Models for Insect Detection System with Field Adaption. Agriculture 2023, 13, 741. https://doi.org/10.3390/agriculture13030741

Minor Spell & Grammatical check is required.