Research on Methods of Physical Aided Education Based on Deep Learning

Su, Wei; Feng, Jian

doi:10.1155/2022/6447471

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…However, the algorithm exhibits poor robustness and struggles to cope with the challenges of complex human pose estimation in current scenarios. Su et al [9] (2022) proposed a motion pose estimation algorithm based on OpenPose and trained it using the COCO dataset. Through comparison with standard poses, the study demonstrated the algorithm's ability to accurately recognize various badminton action poses, with a recognition rate of up to 94%.…”

Section: Contribution Of This Articlementioning

confidence: 99%

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

Ding,

Niu,

Nie

et al. 2024

Sensors

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In response to the numerous challenges faced by traditional human pose recognition methods in practical applications, such as dense targets, severe edge occlusion, limited application scenarios, complex backgrounds, and poor recognition accuracy when targets are occluded, this paper proposes a YOLO-Pose algorithm for human pose estimation. The specific improvements are divided into four parts. Firstly, in the Backbone section of the YOLO-Pose model, lightweight GhostNet modules are introduced to reduce the model’s parameter count and computational requirements, making it suitable for deployment on unmanned aerial vehicles (UAVs). Secondly, the ACmix attention mechanism is integrated into the Neck section to improve detection speed during object judgment and localization. Furthermore, in the Head section, key points are optimized using coordinate attention mechanisms, significantly enhancing key point localization accuracy. Lastly, the paper improves the loss function and confidence function to enhance the model’s robustness. Experimental results demonstrate that the improved model achieves a 95.58% improvement in mAP50 and a 69.54% improvement in mAP50-95 compared to the original model, with a reduction of 14.6 M parameters. The model achieves a detection speed of 19.9 ms per image, optimized by 30% and 39.5% compared to the original model. Comparisons with other algorithms such as Faster R-CNN, SSD, YOLOv4, and YOLOv7 demonstrate varying degrees of performance improvement.

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Section: Contribution Of This Articlementioning

confidence: 99%