Authors:
Muhammad Abdul Haq
1
;
2
;
Shuhei Tarashima
3
and
Norio Tagawa
1
Affiliations:
1
Faculty of Systems Design, Tokyo Metropolitan University, Tokyo, Japan
;
2
Department of Information Technology, Universitas Muhammadiyah Yogyakarta, Yogyakarta, Indonesia
;
3
Innovation Center, NTT Communications Corporation, Tokyo, Japan
Keyword(s):
Badminton Racket Dataset, Annotated Sports Dataset, Object Detection Dataset.
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, th
e evaluation process, and insights into its application in enhancing automated detection in badminton. The dataset is available at https://github.com/muhabdulhaq/racketdb.
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