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Target detection algorithm for basketball robot based on IBN-YOLOv5s algorithm.

Yuan-Hui Li¹, Hong-Bo Yu²

¹Department of Physical Education and Research, Heilongjiang University, Harbin, 150080, China.

Scientific Reports

|December 24, 2024

Summary

This summary is machine-generated.

This study introduces an improved YOLOv5s model for basketball robots to detect target balls. The enhanced model achieved 94.5% accuracy, significantly improving automated sports performance.

Keywords:

Basketball robot Batch normalization Deep learning Target detection YOLOv5s

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Accurate target ball detection is crucial for basketball robots in automated sports.
Existing methods may lack the precision required for intelligent competition.

Purpose of the Study:

To develop an effective target detection method for basketball robots.
To enhance the YOLOv5s model with spatial pyramid pooling and instance-batch normalization.

Main Methods:

Utilized an improved YOLOv5s model incorporating spatial pyramid pooling and instance-batch normalization.
Compared transfer learning (pre-training) with random initialization for model training.
Evaluated different enhancement schemes on the YOLOv5s model.

Main Results:

Transfer learning yielded a mean average precision of 0.918, outperforming random initialization.
The optimized scheme (scheme 2) achieved a detection accuracy of 94.5% on dataset 1.
The proposed algorithm demonstrated effectiveness and accuracy in target detection tasks.

Conclusions:

The improved YOLOv5s model with proposed enhancements is effective for basketball robot target detection.
This research advances basketball robotics and supports efficient automated basketball games.
The developed algorithm provides a technical basis for future intelligent sports systems.