E3S Web Conf.
Volume 233, 20212020 2nd International Academic Exchange Conference on Science and Technology Innovation (IAECST 2020)
|Number of page(s)||5|
|Section||BFS2020-Biotechnology and Food Science|
|Published online||27 January 2021|
Application of Convolution Network Model Based on Deep Learning in Sports Image Information Detection
The Department of Information, Beijing City University, Beijing, China
In recent years, convolution neural network has achieved great success in single image super-resolution detection. Compared with the traditional method, this method achieves better reconstruction detection effect. However, the network structure of the existing reconstruction model is shallow, and the convolution kernel has a small acceptance, so it is difficult to learn a wide range of motion image features, which affects the quality of motion image information detection. Aiming at the problems and shortcomings of the existing sports image information detection based on convolution neural network, this paper proposes the application of convolution network model based on deep learning in sports image information detection. In this paper, we get the average SSIM value from the data of set5, set14, bsd100 and urban100 by using the X4 model of different algorithms. The average SSIM value of set5 is 0.865, which shows that the quality of sports image reconstruction and the reconstruction efficiency of the model can be improved by using the local image features of different scales, which provides technical support for sports image information detection. The research in this paper has important practical significance for the further development of the two and the reform of the convolution network model in sports image information detection.
© The Authors, published by EDP Sciences 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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