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Thank you for your valuable contribution.
[# PeerJ Staff Note - this decision was reviewed and approved by Xiangjie Kong, a PeerJ Section Editor covering this Section #]
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The article deserves attention for publication as it shows comprehensive reports on the YOLO algorithm model compression. This topic is also in high demand in computer vision with deep learning.
It is suitable for the paper to implement the PRISMA method in the literature review.
This topic is of high interest due to applications of computer vision for object detection. The community will benefit from this paper. Although not all publications on the lightweight YOLO model have been covered, you can still add some references.
Please add insight on the Yolo-based object detection applications, provide, e.g., a bar chart/pie chart on different fields of applications, e.g., medical imaging, manufacturing, etc..
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1.Does the review encompass all significant lightweight YOLO variants?
1.Are the selection criteria for including specific models clearly defined and justified within the review?
2.What methodology does the review employ to gather and analyze literature?
Does the review address the challenges and solutions related to deploying these models on edge devices with limited computational resources?
Does the review identify current limitations and propose future research directions for lightweight YOLO models?
Are emerging trends, such as the integration of transformer architectures or novel training techniques, discussed in the context of lightweight object detection?
Some comments:
- YOLO (see-once-only) in abstract, pls correct the meaning.
- in abstract, elaborate why select only 103 papers?
- why authors omit publishers like MDPI and maybe other platforms such as SAGE database, IET etc.?
-the reference search is still not sufficiently comprehensive. Why not to use e.g. Scopus and WoS database as they may cover major publishers as long as indexed journal in SCopus and WoS.
I am sure there are many thousands of papers on YOLO published 2016-2025. The authors may consider to refine papers from 2020-2025 but with more thorough search.
-Many references are seen as "errors" (not visible) in the PDF file. I cannot verify the references listed.
The manuscript does not meet the required publication standards. It lacks adequate literature references. The overall structure is not professionally organized, and essential elements such as well-formatted tables are missing..
It fails to include a detailed discussion on lightweight YOLO models and their underlying mechanisms. Furthermore, the manuscript does not present meaningful comparisons with existing methods.
The discussion is limited to object detection results, without providing broader analysis or insights
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