| Football |
YOLOv8 (Hybrid Enhancements) |
High |
Real-time |
Handles occlusion, real-time tracking |
Issues with distant ball tracking |
| Tennis |
SSD-MobileNet |
Medium-high |
Fast |
Lightweight, CPU-friendly |
Sensitive to lighting |
| Table tennis |
Optimized YOLOv3-Tiny |
Very high |
Very Fast |
Great for small object detection |
Needs high-res input |
| Basketball |
YOLO-T2LSTM |
Very high |
Fast |
Accurate action recognition |
Complex model fusion |
| Golf |
Faster R-CNN |
Very high |
Slow |
High precision tracking |
Not real-time capable |
| Handball |
Mask R-CNN |
Very high |
Slow |
High segmentation accuracy |
High computational cost |
| Volleyball |
YOLOv3 (Enhanced) |
High |
Fast |
Improved detection of fast-moving objects |
Training data limits generalizability |
| Boccia |
Tiny-YOLO |
High |
Fast |
Good color-based accuracy |
Lighting sensitivity, narrow FOV |
| Rugby |
YOLOv3 |
High |
Fast |
Compact architecture for embedded use |
Lower resolution support |
| Padel |
YOLO + Audio SED |
High |
Fast |
Context-aware detection |
Audio interference risks |
| Badminton |
YOLO-HGNet |
Very high |
Fast |
Enhanced classification and detection accuracy |
Requires tuning and large datasets |
