Abstract
Moving target detection is involved in many engineering projects, but it is difficult because of the strong time-varying speed and uncertain path. Goal recognition is the key technology of the basketball goal automatic test. Also, accurate and timely judgment of basketball goals has important practical value. Therefore, a basketball goal recognition method based on an improved lightweight deep learning network model (L-MobileNet) is proposed. First of all, the basket detection is carried out by the Hough circle transform algorithm. Then, in order to further improve the detection speed of basketball goals, based on the lightweight network MobileNet, an improved lightweight network (L-MobileNet) is proposed. First of all, for deeply separable convolution, channel compression and block convolution reduce the parameters and computational complexity of the module. At the same time, because block convolution will hinder the information exchange between characteristic channels, an improved channel shuffling method, IShuffle, is introduced. Then, combined with the residual structure to improve the generalization ability of the network, the RLDWS module is constructed. Finally, a more lightweight network L-MobileNet is constructed by using the RLDWS module. The experimental results show that the proposed method can effectively realize the judgment of basketball goals, and the judgment accuracy is improved by 8.35%. At the same time, the amount of parameters and computation is only 29.7% and 53.2% of the original, and it also has certain advantages compared with other lightweight networks.
Highlights
Introduction e NBA (National Basketball Association) and CBA (China Basketball Association) are popular sports in current ball games. rough the understanding of the NBA and CBA, it is found that they use artificial and intelligent devices to realize timing and scoring. e method of combining artificial and intelligent devices is used to realize timing and scoring. ere is a camera on the backboard of the NBA, which automatically takes photos whenever players are under the basket or on the layup
When the basketball falls from the basket, it will touch the net, which will drive the sensor. e controller will receive the goal information, and the referee will update the score and the time. e competition between the NBA and CBA is dominated by manpower and supplemented by equipment
Erefore, in order to better solve the problems of easy damage, high cost, and misjudgment in traditional fixedpoint shooting devices, this paper proposes a basketball goal recognition method based on image analysis and uses deep learning technology to solve the abovementioned problems
Summary
The target detection methods based on deep learning are mainly divided into two categories [11,12,13,14]: the two-stage detection framework and single-stage detection framework. e two-stage detection framework mainly divides the detection task into two stages. There are some problems such as tedious training steps, slow detection speed, and the need to input fixed-size images He et al [17] proposed an SPPNet algorithm to solve the problem that the fixed-size image must be input to extract features by the CNN. E main innovation of this network lies in the proposal of the Depth-Wise Convolution (DWC) module to reduce the parameters and computation and the effective compromise between classification accuracy and speed by using two superparameters of width multiplier and resolution multiplier. The problems of the existing lightweight network model MobileNet are analyzed theoretically, and the improved strategies are put forward to solve these problems, and the RLDWS module is gradually constructed. A comparative experiment with other lightweight networks is carried out to further verify the rationality and effectiveness of the proposed improved network
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