Lightweight Tennis Ball Detection Algorithm Based on Robomaster EP

Abstract

To address the problems of poor recognition effect, low detection accuracy, many model parameters and computation, complex network structure, and unfavorable portability to embedded devices in traditional tennis ball detection algorithms, this study proposes a lightweight tennis ball detection algorithm, YOLOv5s-Z, based on the YOLOv5s algorithm and Robomater EP. The main work is as follows: firstly, the lightweight network G-Backbone and G-Neck network layers are constructed to reduce the number of parameters and computation of the network structure. Secondly, convolutional coordinate attention is incorporated into the G-Backbone to embed location information into channel attention, which enables the network to obtain location information of a larger area through multiple convolutions and enhances the expression ability of mobile network learning features. In addition, the Concat module in the original feature fusion is modified into a weighted bi-directional feature pyramid W-BiFPN with settable learning weights to improve the feature fusion capability and achieve efficient weighted feature fusion and bi-directional cross-scale connectivity. Finally, the Loss function EIOU Loss is introduced to split the influence factor of the aspect ratio and calculate the length and width of the target frame and anchor frame, respectively, combined with Focal-EIOU Loss to solve the problem of imbalance between complex and easy samples. Meta-ACON’s activation function is introduced to achieve an adaptive selection of whether to activate neurons and improve the detection accuracy. The experimental results show that compared with the YOLOv5s algorithm, the YOLOv5s-Z algorithm reduces the number of parameters and computation by 42% and 44%, respectively, reduces the model size by 39%, and improves the mean accuracy by 2%, verifying the effectiveness of the improved algorithm and the lightweight of the model, adapting to Robomaster EP, and meeting the deployment requirements of embedded devices for the detection and identification of tennis balls.