YOLOv8-PoseBoost: Advancements in Multimodal Robot Pose Keypoint Detection

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper proposed a Yolov8-poseboost model by adding the CBAM and the cross-level connectivity channels to improve the pose detection accuracy of small targets. The proposed model achieves better performance under the COCO dataset and MPII dataset as compared to the existing methods. My comments on this paper are summarized as below:

 

(1)   The performance of the proposed Yolov8-PoseBoost model is good with higher accuracy and lower complexity and model size as compared to some of the SOTA methods. I would suggest the authors to compare the proposed model with the existing SOTA design, like RTMpose and DWPose, to see the performance of the proposed design and do some evaluation.

(2)   Since the proposed model is claimed to deal with the pose detection of small targets, it is suggested that the authors should evaluate the performance of the proposed model under the test sequences having small targets to demonstrate the claimed performance.

(3)   Since the proposed model is a light-weight model that should be good for realization in an embedded system, it is suggested that the authors should evaluate the processing performance of the proposed model under an embedded system to show its low-complexity and low model size advantages.

Comments on the Quality of English Language

None

Author Response

This paper proposed a Yolov8-poseboost model by adding the CBAM and the cross-level connectivity channels to improve the pose detection accuracy of small targets. The proposed model achieves better performance under the COCO dataset and MPII dataset as compared to the existing methods.

Dear Reviewer,

Thank you very much for your valuable feedback. Your insights are greatly appreciated, and we have carefully considered your suggestions. We have made the necessary revisions to the manuscript, and your comments have been highlighted in red for easy reference. Should you have any further questions or require additional clarification, please do not hesitate to contact us. Once again, we sincerely appreciate your thoughtful review.

 

My comments on this paper are summarized as below:

The performance of the proposed Yolov8-PoseBoost model is good with higher accuracy and lower complexity and model size as compared to some of the SOTA methods. I would suggest the authors to compare the proposed model with the existing SOTA design, like RTMpose and DWPose, to see the performance of the proposed design and do some evaluation.

Response:

We appreciate the reviewer's suggestion to compare our proposed Yolov8-PoseBoost model with existing state-of-the-art (SOTA) designs such as RTMpose and DWPose. We agree that such a comparison would provide valuable insights into the performance of our model. In our revised version, we will include a comparative evaluation section where we will benchmark the performance of our model against these SOTA methods, focusing on accuracy, complexity, and model size. Thank you for this valuable recommendation, and we will ensure to address it in the revised manuscript.

 

(2)   Since the proposed model is claimed to deal with the pose detection of small targets, it is suggested that the authors should evaluate the performance of the proposed model under the test sequences having small targets to demonstrate the claimed performance.

Response:

We appreciate the reviewer's suggestion. In our study, we deliberately examined the effectiveness of our model in detecting small targets by evaluating its performance on datasets such as COCO and MPII, which contain instances of small-sized objects. We found that our model demonstrates robustness and accuracy even when faced with challenging scenarios involving small targets.

 

(3)   Since the proposed model is a light-weight model that should be good for realization in an embedded system, it is suggested that the authors should evaluate the processing performance of the proposed model under an embedded system to show its low-complexity and low model size advantages.

Response:

We appreciate the reviewer's insightful suggestion. Evaluating the processing performance of our proposed model under embedded systems to demonstrate its advantages in low-complexity and small model size is indeed an important aspect for future research. We have taken note of this suggestion and plan to prioritize this task in our future work. Additionally, we have included a discussion on future work in the conclusion section of the manuscript. We are grateful for the valuable input provided by the reviewer.

 

Reviewer 2 Report

Comments and Suggestions for Authors

The Manuscript requires major revision  

Comments for author File: Comments.pdf

Author Response

This paper introduces the YOLOv8-PoseBoost model, which enhances the network’s ability to focus on small targets and increase sensitivity to small-sized pedestrians by incorporating the CBAM attention mechanism module, employing multiple scale detection heads, and optimizing the bounding box regression loss function.

 

Dear Reviewer,

Thank you very much for your valuable feedback. Your insights are greatly appreciated, and we have carefully considered your suggestions. We have made the necessary revisions to the manuscript, and your comments have been highlighted in red for easy reference. Should you have any further questions or require additional clarification, please do not hesitate to contact us. Once again, we sincerely appreciate your thoughtful review.

 

My comments are appended below:

1. Theauthors can draw the Robot Architecture (Hardware, Software): what kind of robot is applied ( industrial robot, manipulator, drone)

Response：

Thank you for your insightful suggestion.

While we appreciate the importance of discussing robot architecture, it's worth noting that the focus of our current work primarily revolves around pose estimation rather than the intricacies of robot architecture. In our introduction section, we have provided a discussion on relevant background information. However, we have concentrated on the core aspect of pose estimation for the scope of this paper. Should there be a need for a more comprehensive exploration of robot architecture in future works, we would certainly consider incorporating such discussions.

2. Theauthors tested also ImageNet as AI model ?

Response：

We sincerely appreciate your question. However, we did not utilize the ImageNet dataset in our testing process. If you believe that leveraging this dataset would be beneficial for further evaluating the accuracy and robustness of our model, please feel free to let us know. We are more than willing to incorporate it into our future testing procedures to enhance the comprehensiveness of our analysis.

3. Doesthe YOLOv8-PoseBoost find application in Human Activity Recognition(HAR) as well ?

Response：

Thank you for the insightful question regarding the potential application of the YOLOv8-PoseBoost model in Human Activity Recognition (HAR). While our current focus remains on pose detection, it is worthwhile to explore the broader applicability of our model in HAR tasks and consider it as a key aspect of our future work.

 

Human Activity Recognition involves detecting, classifying, and interpreting human actions or activities based on various types of input data, such as video streams, sensor data, or even skeletal pose estimations. Given that accurate pose estimation is a crucial component of HAR systems, the robust and efficient pose detection capabilities offered by YOLOv8-PoseBoost could serve as a valuable input feature for HAR algorithms.

 

By leveraging the accurate spatial-temporal information provided by our model, HAR systems could potentially achieve higher accuracy and robustness in recognizing human activities. For example, the pose estimations obtained from YOLOv8-PoseBoost could be used to extract features representing key body movements or postures, which can then be fed into machine learning algorithms or deep neural networks for activity classification.

 

Furthermore, the lightweight nature of our model makes it well-suited for deployment in resource-constrained environments, such as embedded systems or edge devices, which are commonly used for real-time HAR applications. The low computational complexity and small model size of YOLOv8-PoseBoost enable efficient inference on these devices, facilitating real-time activity recognition in various scenarios, including smart homes, healthcare monitoring, and sports analytics.

 

In our future work, we intend to prioritize the exploration of YOLOv8-PoseBoost's application in Human Activity Recognition as a central research focus. We will dedicate resources to investigating and validating its effectiveness in HAR tasks through extensive experimentation and evaluation. This includes optimizing the model architecture and training procedures specifically for HAR, as well as developing novel algorithms or techniques to leverage the pose information for activity recognition.

 

Additionally, we will actively collaborate with researchers and practitioners in the field of Human Activity Recognition to ensure that our work addresses relevant challenges and contributes meaningfully to the advancement of HAR technology. By placing a strong emphasis on this area in our future research agenda, we aim to establish YOLOv8-PoseBoost as a valuable tool for real-world applications in HAR and beyond.

4. Theauthors can discussi if there is any intersection with Augmented

Response：

Thank you for your inquiry about the potential connection between our YOLOv8-PoseBoost model and Augmented Reality (AR). While our current focus centers on pose detection, exploring the integration of our model with AR presents an exciting opportunity for future exploration and innovation.

 

Augmented Reality, as you know, involves overlaying digital content onto the real world, typically through devices like smartphones, tablets, or AR glasses. A critical aspect of AR applications is the ability to accurately understand and interact with the user's environment, often requiring real-time detection and tracking of objects, including human poses.

 

Our YOLOv8-PoseBoost model could significantly enhance AR systems' capabilities, particularly in scenarios involving user interaction. By providing precise and reliable pose estimations in real-time, our model could enable more immersive and intuitive AR experiences that dynamically respond to users' movements and gestures.

 

For instance, in AR-based gaming or fitness applications, our model could track users' body movements and gestures with high accuracy, facilitating more natural and responsive gameplay or exercise guidance. Similarly, in AR-assisted navigation or training scenarios, accurate pose detection could help users interact more effectively with virtual objects or receive real-time instructions based on their actions.

 

Moreover, the lightweight design of our model makes it well-suited for integration into AR devices with limited computational resources, ensuring efficient performance even in mobile or wearable AR platforms.

 

In our future endeavors, we plan to delve deeper into the potential applications of YOLOv8-PoseBoost in Augmented Reality. This includes exploring how our model can enhance existing AR systems and developing new AR experiences that leverage pose detection for improved interaction and immersion.

 

By actively exploring the convergence of our work with Augmented Reality, we aim to advance AR technology and create innovative solutions that redefine how users engage with digital content in the physical world.

5. Inthe context oh Human Robot Interaction(HRI) is there any implication for Robotic Surgery ?

Response：

Thank you for your insightful feedback.

After reviewing the literature, we acknowledge the importance of considering the implications of robotic surgery in future research endeavors. The intersection of robotics and surgery presents a wealth of opportunities for advancing medical technology and improving patient outcomes. We plan to explore the impact of robotic surgery on various aspects of healthcare delivery and patient care in our future studies.

6. Didthe author applied ROS(Robot Operation System) framework for the implementation system ?

Response：

Thank you very much for your question. We regret to inform you that the ROS (Robot Operating System) framework was not utilized in our implementation system. We acknowledge the importance and advantages of employing ROS in robotics research, and we recognize that its absence may be considered a limitation of our current work. In future endeavors, we plan to address this by incorporating ROS into our framework to capitalize on its capabilities for improved system functionality and adaptability.

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

the manuscript can be accepted for the pubblication