Tenis sporcularında görüntü işleme teknikleri kulllanılarak yapay zekâ tabanlı performans analiz sistemi geliştirilmesi
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
İnönü Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Tenis Sporcularında Görüntü İşleme Teknikleri Kullanılarak Yapay Zekâ Tabanlı Performans Analiz Sistemi Geliştirilmesi Amaç: Bu çalışma, tenis oynayan sporcuların teknik performanslarının nesnel biçimde değerlendirilmesini sağlamak amacıyla yapay zekâ destekli görüntü işleme yöntemlerini esas alan bir değerlendirme sistemi geliştirmeyi hedeflemektedir. Bu sistem, görüntü işleme tekniklerinden biri olan pose estimation yaklaşımı ile tenis hareketlerini detaylı biçimde analiz etmeyi; aynı zamanda ITN testi gibi objektif saha değerlendirmeleri ile elde edilen bulguları birleştirerek sistemin geçerliliğini ve güvenilirliğini ortaya koymayı amaçlamaktadır. Materyal ve Metot: Çalışmaya katılım koşullarını sağlayan 15 sporcu dâhil olmuştur. Araştırma, nicel ve deneysel bir yöntemle yürütülmüştür. Bunun için, tenis oynayan sporcuların çeşitli açılardan çekilmiş video görüntüleri toplanmış ve bu kayıtlar, yapay zekâ destekli derin öğrenme modelleri aracılığıyla analiz edildi. Özellikle YOLOv8-Pose adlı güncel bir model tercih edildi. Bulgular: Araştırma kapsamında geliştirilen bilgisayarla görüş tabanlı poz tahmini sistemi, tenis sporcularının hareket dinamiklerini analiz etmek amacıyla kullanılmış ve elde edilen sonuçlar, modelin hem teknik doğruluğunu hem de uygulama potansiyelini ortaya koymuştur. Sistem, performans metrikleri ve biyomekanik değerlendirmeler açısından tatmin edici sonuçlar üretmiş; sporcuların teknik davranışlarının nesnel verilerle analizine önemli katkı sağladı. Elde edilen mAP@50 (0,941) ve mAP@50–95 (0,772) değerleri, modelin basit ve karmaşık pozisyonları yüksek doğrulukla tespit edebildiğini göstermektedir. Ortalama precision (0,867) ve recall (0,884) skorları ise modelin hem yanlış pozitif tahminleri azalttığını hem de gerçek pozları başarıyla yakaladığını ortaya koymaktadır. Sonuç: Bu çalışma, YOLOv8-Pose modelinin tenis sporcularının hareket dinamiklerini analiz etmede hem teknik doğruluk hem de uygulama pratikliği açısından güçlü bir araç olduğunu ortaya koymuştur. Model, karmaşık vuruş desenlerini yüksek hassasiyetle tanımlayabilmiş, özellikle mAP, precision ve recall değerleri bakımından başarılı bir performans sergiledi.
Development of an AI-Based Performance Analysis System for Tennis Athletes Using Image Processing Techniques Aim: This study aims to develop an evaluation system based on artificial intelligence-supported image processing methods to ensure the objective assessment of the technical performance of tennis players. This system aims to analyze tennis movements in detail using the pose estimation approach, which is one of the image processing techniques; at the same time, it seeks to demonstrate the validity and reliability of the system by combining the findings obtained from objective field assessments such as the ITN test. Material and Method: Fifteen athletes who met the participation criteria were included in the study. The research was conducted using a quantitative and experimental method. For this purpose, video footage of tennis players taken from various angles was collected, and these recordings were analyzed through AI-supported deep learning models. Especially the current model named YOLOv8-Pose was preferred. Result: The computer vision-based pose estimation system was developed as part of the research was used to analyze the movement dynamics of tennis players, and the results obtained demonstrated both the technical accuracy and application potential of the model. The system produced satisfactory results in terms of performance metrics and biomechanical evaluations, contributing significantly to the objective analysis of athletes' technical behaviors. The obtained mAP@50 (0,941) and mAP@50–95 (0,772) values demonstrate that the model can detect both simple and complex positions with high accuracy. The average precision (0,867) and recall (0,884) scores reveal that the model both reduces false positive predictions and successfully captures real poses. Conclusion: This study has demonstrated that the YOLOv8-Pose model is a powerful tool for analyzing the movement dynamics of tennis players, both in terms of technical accuracy and practical application. The model has been able to identify complex stroke patterns with high precision, particularly demonstrating successful performance in terms of mAP, precision, and recall values.
Development of an AI-Based Performance Analysis System for Tennis Athletes Using Image Processing Techniques Aim: This study aims to develop an evaluation system based on artificial intelligence-supported image processing methods to ensure the objective assessment of the technical performance of tennis players. This system aims to analyze tennis movements in detail using the pose estimation approach, which is one of the image processing techniques; at the same time, it seeks to demonstrate the validity and reliability of the system by combining the findings obtained from objective field assessments such as the ITN test. Material and Method: Fifteen athletes who met the participation criteria were included in the study. The research was conducted using a quantitative and experimental method. For this purpose, video footage of tennis players taken from various angles was collected, and these recordings were analyzed through AI-supported deep learning models. Especially the current model named YOLOv8-Pose was preferred. Result: The computer vision-based pose estimation system was developed as part of the research was used to analyze the movement dynamics of tennis players, and the results obtained demonstrated both the technical accuracy and application potential of the model. The system produced satisfactory results in terms of performance metrics and biomechanical evaluations, contributing significantly to the objective analysis of athletes' technical behaviors. The obtained mAP@50 (0,941) and mAP@50–95 (0,772) values demonstrate that the model can detect both simple and complex positions with high accuracy. The average precision (0,867) and recall (0,884) scores reveal that the model both reduces false positive predictions and successfully captures real poses. Conclusion: This study has demonstrated that the YOLOv8-Pose model is a powerful tool for analyzing the movement dynamics of tennis players, both in terms of technical accuracy and practical application. The model has been able to identify complex stroke patterns with high precision, particularly demonstrating successful performance in terms of mAP, precision, and recall values.
Açıklama
Anahtar Kelimeler
Spor, Sports
