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Basketball detection based on YOLOv8.

Zeyu Liang1,2, Jiuyuan Wang3, Tianhao Huang1

  • 1School of Chinese Basketball, Beijing Sport University, Beijing, China.

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A new real-time basketball detection model, BGS-YOLO, improves accuracy and robustness. It uses advanced features like BiFPN and attention mechanisms for better performance in complex sports scenes.

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Area of Science:

  • Computer Vision
  • Sports Analytics
  • Machine Learning

Background:

  • Accurate basketball detection is vital for sports analytics, coaching, and fan experience.
  • Existing methods struggle with scale variations, scene complexity, and camera angle changes, limiting real-time performance.
  • Automated systems require enhanced accuracy and robustness for practical applications.

Purpose of the Study:

  • Introduce BGS-YOLO, a novel real-time basketball detection model.
  • Address limitations of current technologies in accuracy and real-time detection.
  • Enhance feature extraction, attention, and robustness for improved basketball identification.

Main Methods:

  • Integrated Bidirectional Feature Pyramid Network (BiFPN) for multi-resolution feature merging.
  • Incorporated Global Attention Mechanism (GAM) to optimize feature focus in complex scenes.
  • Utilized SimAM-C2f to calculate target-background similarity, reducing false positives.

Main Results:

  • BGS-YOLO achieved a mean average precision (mAP) of 93.2%, outperforming existing models.
  • Global Attention Mechanism (GAM) boosted recall in occluded scenarios by 3.2%.
  • SimAM-C2f reduced false positives by 15%, enhancing detection reliability.

Conclusions:

  • BGS-YOLO significantly improves basketball detection accuracy and robustness.
  • The model offers valuable technical support for intelligent sports analytics and real-time applications.
  • Innovations in feature fusion and attention mechanisms contribute to superior performance.