Automatic Detection of Image Features in Basketball Shooting Teaching Based on Artificial Intelligence

Abstract

In order to weaken the limitation between the adjacent nodes of shooting action teaching image and fully expose the necessary image features, an automatic detection method of shooting action teaching image features based on artificial intelligence is proposed. Starting from the decomposition of shooting teaching action, the practical attribute of shooting teaching image is defined, and then the standardized image detection environment based on artificial intelligence is built according to the action essentials of consistent shooting. On this basis, through the way of extracting the characteristics of teaching image, calculating the characteristic scale value of shooting action, combining with the feature description principle of automatic detection, the design of automatic detection method of shooting action teaching image characteristics is completed. The experimental results show that, compared with the traditional feature method, the UTI traction coefficient between the adjacent nodes of the image increases to 0.33, while the matching time required for detection decreases to 17.5 mm, so as to fully expose the necessary features of the shooting action teaching image.

Concrete Application of Computer Virtual Image Technology in Modern Sports Training

“…where P z represents the number of correctly detected goals and P j represents the number of detected goals. e number of shots is set to 45. e methods of [8], the methods of [9], and the proposed methods are used to detect the basketball shooting training goal image, respectively. e comparison results of the detection accuracy of basketball shooting training goals of different methods are calculated according to formula (25), as shown in Figure 6.…”

“…It can be seen from Figure 6 that when the number of shots is 40, the average detection accuracy of basketball shooting training goals of the methods of [ 8 ] is 81.2%, the average detection accuracy of basketball shooting training goals of the methods of [ 9 ] is 74.5%, and the average detection accuracy of basketball shooting training goals of the proposed method is as high as 92.8%. It can be seen that the accuracy of basketball shooting training goal detection of the proposed method is high, which can effectively improve the accuracy of basketball shooting training goal detection.…”

“…Taking the detection time as the evaluation index, the shorter the detection time, the higher the detection efficiency of the method. Using the methods of [ 8 ], the methods of [ 9 ], and the proposed methods, the basketball shooting training goal images are detected, and the comparison results of the detection efficiency of basketball shooting training goals of different methods are obtained, as shown in Figure 7 .…”

“…At present, scholars in related fields have made some progress in basketball shooting detection. Yu and Liu [ 8 ] proposed an automatic feature detection method of the basketball shooting teaching image based on artificial intelligence. Starting from the decomposition of basketball shooting teaching actions, the practical attributes of basketball shooting teaching images are defined, and a standardized image detection environment based on artificial intelligence is constructed according to the essentials of basketball shooting actions.…”

Application of Artificial Intelligence in Basketball Sport

“…The author believed that this method could provide a data reference for basketball training [32]. Yu and Liu proposed an AIbased method for the automatic detection of shooting motion image features, which may be helpful to the training of basketball shooting [33].…”