Lightweight Network Detection Model for Table Tennis Balls Based on Improved YOLOv5
Ball games are the most popular sports in traditional sports competitions,and the target detection of balls can be used to improve the analysis of sports games,the security of surveillance systems,and the realism of Virtual Reality experiences.YOLOv5,as an excellent single-stage detection algorithm,is one of the most frequently used target detection algorithms in the field of computer vision in recent years due to its easy platform portability and simple detection steps.However,the YOLOv5 model has a large number of parameters.In order to reduce the number of parameters so that it can be ported to other platforms faster,this paper proposes a lightweight and improved YOLOv5 algorithm,which takes YOLOv5s as the base model,and reduces the amount of computation and improves the accuracy by the methods of replacing the backbone network with the improved MobileNetv3,introducing the CBAM Attention Mechanism in the neck,and improving the C3 module.The improved model is verified after the training,and the experimental results show that the number of parameters of the improved detection algorithm roughly decreases by 65%,and the average accuracy improves by 0.5%,which meets the accuracy requirements and real-time performance of practical application scenarios for table tennis.
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