Multi-Shadow Scenarios Tennis Ball Detection by an Improved RTMdet-Light Model

Abstract

The real-time and rapid recording of sport sensor data related to tennis ball trajectories facilitates the analysis of this information and the development of intelligent training regimes. However, there are three essential challenges in the task of tennis ball recognition using sport vision sensors: the small size of the ball, its high speed, and the complex match scenarios. As a result, this paper considers a lightweight object detection model named improved RTMDet-light to deal with these challenges. Specifically, it has compatible capacities in the backbone and neck, constructed by a basic building block that consists of large-kernel depth-wise convolutions. Furthermore, GhosNet and ShuffleNet are used to replace the CSPLayers which reduce the parameters of our model. The lightweight model proposed addresses the inherent challenges of detecting small objects and muti scenarios in the match. After training, the proposed model performed better on four scenarios with different shades of tennis ball match, with results visualized through heatmaps and performance metrics tabulated for detailed analysis. The recall, FLOPs and number of parameters of the improved RTMDet-light are 71.4%, 12.543G, and 4.874M, respectively. The results demonstrate robustness and effectiveness of our model in accurate tennis ball detecting across various scales. In conclusion, our model for real-time detection in tennis ball detection offers a lightweight and faster solution for sport sensors.