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Tennis ball detection based on YOLOv5 with tensorrt.

Xiulei Zhang¹, Bin Li²

¹College of Physical Education, Jilin Normal University, Siping, 136000, China.

Scientific Reports

|July 2, 2025

Summary

This study introduces an advanced hawk eye detection system for tennis, utilizing YOLOv5 and TensorRT. The new method significantly enhances the accuracy and speed of tennis ball drop detection for fair match penalization.

Keywords:

Hawk eye system Object detection Tennis TensorRT YOLOv5

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Area of Science:

Sports Technology
Computer Vision
Artificial Intelligence

Background:

Tennis penalization requires high accuracy and fairness.
Current methods for detecting ball drops can be slow and inaccurate.
Technological solutions are needed to improve officiating in tennis.

Purpose of the Study:

To develop an accurate and fast hawk eye detection system for tennis.
To leverage deep learning for real-time ball tracking and detection.
To provide reliable technical support for tennis match officiating.

Main Methods:

Utilized YOLOv5 for efficient tennis ball feature extraction and target detection.
Integrated TensorRT for inference acceleration, optimizing real-time performance.
Implemented layer fusion and memory optimization techniques within the TensorRT framework.

Main Results:

Achieved 94% mean average precision in tennis ball detection.
Recorded a combined detection error of 0.39 meters.
Demonstrated a minimum computing time of 2.28 seconds for detection.

Conclusions:

The proposed hawk eye detection method significantly improves accuracy and speed in tennis ball drop detection.
This system offers reliable technical support for fair and efficient penalization in tennis matches.
The integration of YOLOv5 and TensorRT provides a robust solution for real-time sports analytics.