RacketDB: A Comprehensive Dataset for Badminton Racket Detection

Muhammad Abdul Haq, Muhammad Abdul Haq, Shuhei Tarashima, Norio Tagawa

2025

Abstract

In this paper, we present RacketDB, a specialized dataset designed to address the challenges of detecting badminton rackets in images. This task often hindered by the lack of dedicated datasets. Existing general-purpose datasets fail to capture the unique characteristics of badminton rackets. RacketDB includes 16,608 training images, 3,175 testing images, and 2,899 validation images, all meticulously annotated to enhance object detection performance for sports analytics. To evaluate the effectiveness of RacketDB, we utilized several established object detection models, including YOLOv5, YOLOv8, DETR, and Faster R-CNN. These models were assessed based on metrics like mean average precision (mAP), precision, recall, and F1. Our results demonstrate that RacketDB significantly improves detection accuracy compared to general datasets, highlighting its potential as a valuable resource for developing advanced sports analytics tools. This paper provides a detailed description of RacketDB, the evaluation process, and insights into its application in enhancing automated detection in badminton. The dataset is available at https://github.com/muhabdulhaq/racketdb.

Paper Citation

in Harvard Style

Haq M., Tarashima S. and Tagawa N. (2025). RacketDB: A Comprehensive Dataset for Badminton Racket Detection. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 426-433. DOI: 10.5220/0013159700003912

in Bibtex Style

@conference{visapp25,
author={Muhammad Haq and Shuhei Tarashima and Norio Tagawa},
title={RacketDB: A Comprehensive Dataset for Badminton Racket Detection},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={426-433},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013159700003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - RacketDB: A Comprehensive Dataset for Badminton Racket Detection
SN - 978-989-758-728-3
AU - Haq M.
AU - Tarashima S.
AU - Tagawa N.
PY - 2025
SP - 426
EP - 433
DO - 10.5220/0013159700003912
PB - SciTePress