ArticleScientific reports2024
Optimization of table tennis target detection algorithm guided by multi-scale feature fusion of deep learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Enhancing perceptual consistency and calibrating uncertainty for real-time table tennis ball detection.Scientific reports · 2025Article
- Tennis ball detection based on YOLOv5 with tensorrt.Scientific reports · 2025Article
- Article
- Verification and application of deep learning models in daily sports activities of teenagers.PloS one · 2025Article
- Fast moving table tennis ball tracking algorithm based on graph neural network.Scientific reports · 2024Article
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Authors and funding
1 author.
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Abstract
This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper, DL technology is used to improve the accuracy of table tennis TD through MFF guidance. Initially, based on the FAST Region-based Convolutional Neural Network (FAST R-CNN), the TD is carried out in the table tennis match. Then, through the method of MFF guidance, different levels of feature information are fused, which improves the accuracy of TD. Through the experimental verification on the test set, it is found that the mean Average Precision (mAP) value of the target detection algorithm (TDA) proposed here reaches 87.3%, which is obviously superior to other TDAs and has higher robustness. The DL TDA combined with the proposed MFF can be applied to various detection fields and can help the application of TD in real life.
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