PONE-D-21-39942Improving object detection quality with structural constraintsPLOS ONE

Dear Dr. Rong,

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PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper proposes an approach that utilizes Structural Constraints to improve performance of object detection tasks. The proposed Structural Constraints measurement consists of two parts: Fisher loss and Equi-proportion loss. The fisher loss is helpful in improving classification performance, and the equip loss function is designed for improving localization performance. Finally, a series of strategies are proposed to apply the Structural Constraints into different frameworks of object detection. Extensive experiments are conducted to demonstrate the effectiveness of proposed approach.

1) Positive Points.

Most of the modern object detection networks constrain only classification loss and localization loss, and this paper states that these losses are not enough and then further utilizes the Fisher loss and the Equip loss. Such a solution is interesting and could improve performance of object detection. In addition, the presentation is clear and the paper is easy to read.

2) Negative Points.

The main contribution of this paper is the Structural Constraints that introduce the Fisher loss and the Equip loss. However, the experiments are insufficient to verify the positive effects of the two losses. The ablation experiments on RetinaNet some scores become even worse after using the Fisher loss (Table 1 and Table 2), making it somehow useless for the one-stage object detection framework. In addition, the ablation experiments on Cascade RCNN* show that the Equip loss only improves the AR indexes but becomes harmful for the AP indexes in most cases, implying that the Equip loss is not useful for the multi-stage object detection network. The authors should explain and further justify the positive effects of these two losses.

Another concern is that the overall performance is not very satisfactory (i.e., not state-of-the-art). Many superior object detection networks have been proposed in recent years (e.g., DETR). Although the proposed approach claims that their solution can be applied to all object detection network architectures, more comparative experiments on recent object detection works should be done to demonstrate the effect of proposed approach.

Reviewer #2: Object detection is an important research direction in computer vision and the basis of many downstream work.

In this paper, the author investigated a large number of literatures and found that in object detection, in addition to the commonly used classification loss and regression loss, the addition of other loss functions can improve the detection effect, such as focal loss. At the same time, from the analysis of clustering and other tasks, it is found that the constraint based on the mutual relations between training samples can effectively improve feature learning. Therefore, the authors propose to use structural constraint mechanism in object detection, and improve the effect of object detection by adding structural constraint loss to object detection algorithm. The authors add Fisher loss to the classification branch of object detection and Equi-proportion loss to the location branch, and completes the corresponding training with the help of an proxy feature. Experiments on Object detection public data sets mscoco2017 and Kitti show the effectiveness of this method.

The main problems are as follows:

1、 In this paper, the two additional loss functions are directly added to the original loss function, such as formula 4, but in the following 3.3, it is explained that they are used in different branches, and they are inconsistent.

2、 After adding the loss function, how to train the network? Is it to train with the classification loss first, and then use the Fisher loss for fine-tuning? It needs to be explained in detail here.

3、 The super parameters in the experimental settings, such as the setting of IOU threshold, directly give specific values, but do not explain how to select this parameter value. Further clarification is required.

4、 What is the difference between the Fisher loss added in this paper and the Fisher discriminative layer in reference paper 2.

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Reviewer #1: Yes: Jia Li

Reviewer #2: No

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Improving object detection quality with structural constraints

PONE-D-21-39942R1

Dear Dr. Rong,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Jie Zhang

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Most of my concerns have been well addressed. I think the paper can now be published in its current form.

Reviewer #2: This manuscript has been well improved, and I think all of my comments have been addressed with new analysis.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Jia Li

Reviewer #2: No