Basketball detection based on YOLOv8
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View abstract on PubMed
Summary
This summary is machine-generated.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.
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.
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