Detecting and classifying media images of athletes using convolutional neural networks – case study: Individual sports images

• Autorzy: Mizher Haider Shaker , Taher Montadar Abas , Falih Bashar Saad , Gierz Łukasz Adam , Warguła Łukasz , Wieczorek Bartosz

Identyfikatory

DOI

10.12913/22998624/199798

• Języki publikacji: EN

Abstrakty

EN

Sports image classification using neural networks and machine vision is a rapidly expanding field, with applications in highlight reel creation, performance analysis, and illegal play detection. We present an innovative structure for sports image classification using convolutional neural networks (CNNs) based on deep learning in this article. Boxing, Gymnastics, Swimming, Tennis, and Weight Lifting are five distinct sports that all fall under the umbrella of individual games. In terms of setting and attire, these various forms of athletic competition are very comparable. Specifically, the suggested deep learning model consists of 20-layers, among them, there are four CNN layers. The results show that the proposed model achieved a significant result in terms of accuracy, although the selected sports have similar characteristics to each other. For instance, boxing classification accuracy was 90.63%, gymnastics accuracy was 86.88%, swimming sport image classification achieved 94.06% accuracy, tennis classification accuracy is 88.13%, and the weight lifting was 89.06%, in the testing phase. The obtained results prove that the developed new sports image classification method is effective enough and has been improved.

Słowa kluczowe

EN

CNN; convolutional neural network; deep learning; deep neural network; sport image classification

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2025
• Tom: Vol. 19, no. 6
• Strony: 152--166
• Opis fizyczny: Bibliogr. 47 poz., fig., tab.

Twórcy

autor

Mizher Haider Shaker

• College of Basic Education, University of Diyala, Diyala, Iraq

autor

Taher Montadar Abas

• Department of Communications Engineering, College of Engineering, University of Diyala, Diyala, Iraq

• https://orcid.org/0000-0002-1516-8231

autor

Falih Bashar Saad

• Department of Electrical and Electronics Engineering, Cukurova University, Adana, Turkey
• Department of Computer Techniques Engineering, Al Salam University College, Baghdad, Iraq

• https://orcid.org/0000-0001-6592-0500

autor

Gierz Łukasz Adam

• nstitute of Machine Design, Faculty of Mechanical Engineering, Poznan University of Technology, Piotrowo 3, 60-965 Poznań, Poland

• https://orcid.org/0000-0003-4040-5718

autor

Warguła Łukasz

• nstitute of Machine Design, Faculty of Mechanical Engineering, Poznan University of Technology, Piotrowo 3, 60-965 Poznań, Poland

autor

Wieczorek Bartosz

• nstitute of Machine Design, Faculty of Mechanical Engineering, Poznan University of Technology, Piotrowo 3, 60-965 Poznań, Poland

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).