# Deep Learning-Based Algorithm for Recognizing Tennis Balls
## 1 Introduction  
A national sport that evolved from traditional European aristocratic sports, tennis has become so popular worldwide for its unique spectacle and intense rivalry that it has overtaken basketball and volleyball as the second most popular sport in the world [1]. As tennis continues to grow, more and more people are joining the sport, resulting in many tennis stars and tennis enthusiasts. According to statistics, there are more than 10 million tennis fans and more than 100,000 tennis courts in China, growing at a rate of around 12% per year [2]. The number of people using tennis balls has increased dramatically, so it would take a lot of energy and time to pick up the tennis balls scattered around the court. Moreover, existing ball picking equipment cannot help reduce labor cost well. As a result, the use of robots to automatically identify and pick up tennis balls has become an increasingly popular demand [3, 4]. Among them, there are various methods that can be applied to the autonomous recognition of tennis balls by robots, such as: direct recognition of the color and contour of tennis balls by the camera; and training of images of tennis balls using a cascade classifier on OpenCV. For example, in the literature [5] we relied on the color and contour classifier to recognize the target which is simple and efficient with less preparation, but only guarantees good recognition under ideal conditions such as stable camera operation, open field of view, no interference and clutter, good ambient light, etc., i.e., the anti-interference capability is not strong; then, in the literature [6] we relied on the cascade classifier to recognize the target which improved the anti-interference capability and detection efficiency, but recognition distance is shorter and requires more preparation work. Combining the advantages and disadvantages of the above methods, the deep learning method is chosen to train the image samples of tennis balls, which can ensure the anti-interference ability and recognize tennis balls at longer distances, and the recognition algorithm has strong robustness [7]. Based on deep learning, the corresponding neural network is designed and optimized to improve the recognition rate of the algorithm for tennis balls on outdoor places. For the problem of uneven lighting in outdoor places, the tennis ball image samples in different states are first collected, and then the sample data set is pre-processed [8]; for the problem of complex backgrounds and more people moving in outdoor places, the training data need to be correctly labeled. There exist many simple and effective neural networks for classifying targets and interferers in the input images using deep learning, for example, the improved Faster R-CNN is used in the literature [9] for target recognition, which improves the efficiency of target detection; however, its detection efficiency for small objects is low and there is an overfitting problem. Considering that the experiments described in this paper were conducted outdoors and the camera was moving most of the time, the Mask R-CNN convolutional network with one more mask branch for instance segmentation than the Faster R-CNN was chosen to optimize the recognition tennis algorithm. Mask R-CNN improves the convergence speed of the training model and saves training time by introducing weighted feature fusion of feature layers at different scales and improves the recognition efficiency of tennis balls as much as possible while ensuring the recognition accuracy of the algorithm for tennis balls [10]. In this paper, we design and optimize a deep learning algorithm system for tennis ball recognition that can be applied to intelligent tennis ball picking robots by referring to the literature of machine vision and computer vision.