Article: Intelligent judgement and training optimisation algorithm of table tennis landing area driven by deep learning and computer vision Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2026 Vol.18 No.15 pp.44 - 65 Abstract: In competitive table tennis training, accurate identification and analysis of ball landing points are essential for tactical and technical improvement. This study presents a deep learning-based system integrating high-speed visual perception, three-dimensional trajectory reconstruction, and landing area classification to provide data-driven training optimisation. The core novelty lies in fusing multi-frame time-series modelling with physical rebound constraints and personalised training prescriptions driven by weighted KL divergence. Major technical contributions include small-target enhancement, motion blur compensation, and regional classification error correction. The evaluation protocol involved 418 recorded rounds with high-speed multi-camera setups, achieving a touchdown recognition accuracy of 0.931, regional classification consistency of 0.927, and boundary misjudgement reduction from 0.083 to 0.039. Compared to baseline methods, coverage of deep and corner areas increased from 0.287 to 0.361 and 0.174 to 0.248 respectively, and distribution entropy improved from 2.91 to 3.38, demonstrating substantial quantitative improvements in tactical diversity and training effectiveness. Inderscience Publishers - linking academia, business and industry through research

Title: Intelligent judgement and training optimisation algorithm of table tennis landing area driven by deep learning and computer vision

Authors: Zheming Liu

Addresses: School of Physical, Anshan Normal University, Anshan 114001, China

Abstract: In competitive table tennis training, accurate identification and analysis of ball landing points are essential for tactical and technical improvement. This study presents a deep learning-based system integrating high-speed visual perception, three-dimensional trajectory reconstruction, and landing area classification to provide data-driven training optimisation. The core novelty lies in fusing multi-frame time-series modelling with physical rebound constraints and personalised training prescriptions driven by weighted KL divergence. Major technical contributions include small-target enhancement, motion blur compensation, and regional classification error correction. The evaluation protocol involved 418 recorded rounds with high-speed multi-camera setups, achieving a touchdown recognition accuracy of 0.931, regional classification consistency of 0.927, and boundary misjudgement reduction from 0.083 to 0.039. Compared to baseline methods, coverage of deep and corner areas increased from 0.287 to 0.361 and 0.174 to 0.248 respectively, and distribution entropy improved from 2.91 to 3.38, demonstrating substantial quantitative improvements in tactical diversity and training effectiveness.

Keywords: deep learning; computer vision; table tennis landing point determination; trajectory reconstruction; training optimisation.

DOI: 10.1504/IJRIS.2026.154121

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.15, pp.44 - 65

Received: 08 Feb 2026
Accepted: 21 Apr 2026
Published online: 12 Jun 2026 *

Title: Intelligent judgement and training optimisation algorithm of table tennis landing area driven by deep learning and computer vision

Keep up-to-date