Deep Learning-Based Algorithm for Recognizing Tennis Balls

In this paper, we adjust the hyperparameters of the training model based on the gradient estimation theory and optimize the structure of the model based on the loss function theory of Mask R-CNN convolutional network and propose a scheme to help a tennis picking robot to perform target recognition and improve the ability of the tennis picking robot to acquire and analyze image information. By collecting suitable image samples of tennis balls and training the image samples using Mask R-CNN convolutional network an algorithmic model dedicated to recognizing tennis balls is output; the final data of various loss functions after gradient descent are recorded, the iterative graph of the model is drawn, and the iterative process of the neural network at different iteration levels is observed; finally, this improved and optimized algorithm for recognizing tennis balls is compared with other algorithms for recognizing tennis balls and a comparison is made. The experimental results show that the improved algorithm based on Mask R-CNN recognizes tennis balls with 92% accuracy between iteration levels 30 and 35, which has higher accuracy and recognition distance compared with other tennis ball recognition algorithms, confirming the feasibility and applicability of the optimized algorithm in this paper.