Research on a Dense Pedestrian-Detection Algorithm Based on an Improved YOLO11
Round 1
Reviewer 1 Report
Comments and Suggestions for Authors
Comments for author File: 
Comments.pdf
The English could be improved to more clearly express the research.
Author Response
This text box is not convenient for editing the format. For the detailed responses, please kindly refer to my PDF file. Thank you very much :D !
Author Response File: Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThis work presents an optimised version of YOLO11 to address the problem of insufficient accuracy in pedestrian accuracy in complex scenes. The authors present the C3K2-lighter module by inserting the FasterNet Block into the C3K2 module. To maximise the extraction of local and global features, a Triplet Attention module has been added to reduce problems related to occlusions. Finally, to weigh the various contributions, VFL loss has been added to quantify the variance of the various features between the actual frame and the predicted frame.
*** Strong Points ***
- Good description of the changes made to the module
- Sufficient comparison with the various methods
*** Weak Points ***
- The bibliographic research on the state of the art should be expanded.
- A diagram of the dataset used helps the reader to better understand the differences between the various levels of occlusion
- Further analysis of the model's discriminatory capacity on images with severe occlusion
*** Questions ***
- Why did you only use precision and recall? An analysis of how the model performs on images with severe occlusion should be addressed.
- How was the dataset divided? What is the percentage of labels in the three sets obtained?
- The text states that the loss shows an improvement in performance across a range of experiments. These are not described in detail in the paper. Why is this? Loss is an important contribution and should be expanded upon and described in more detail.
- The text states that loss helps to discriminate between positive and negative samples, facilitating the analysis of different features. The task on which you are performing your classification should be described in more detail in the text.
Author Response
This text box is not convenient for editing the format. For the detailed responses, please kindly refer to my PDF file. Thank you very much :D!
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for Authors
Comments for author File: Comments.pdf
Author Response
This text box is not convenient for editing the format. For the detailed responses, please kindly refer to my PDF file. Thank you very much :D !
Author Response File: Author Response.pdf
