Abstract
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.
Highlights
In recent years, with the development of artificial intelligence, deep learning has shown amazing effect in processing massive data
In the motion image super-resolution algorithm based on convolution neural network, div2k provided by NTIRE 2017 super-resolution challenge is selected as the training data set
(1) the quality of motion image reconstruction is improved compared with other comparison models, this model is based on deep network, which has the problems of high complexity and slow image reconstruction speed, and cannot meet the application scenarios with high real-time requirements
Summary
With the development of artificial intelligence, deep learning has shown amazing effect in processing massive data. In the field of image recognition competition, the accuracy of image recognition based on deep learning algorithm has far exceeded the traditional algorithm. In addition to these achievements, new breakthroughs in deep learning include natural language processing, speech recognition, image semantic segmentation, image recognition, target detection, etc. Analysis shows that the application of deep learning convolution network model in image information detection is still insufficient. The application of convolution network model based on deep learning in sports image information detection is established. In the research, according to the actual situation of sports image information detection, the advantages of convolution network model are analyzed. The effective combination of the two can improve the detection effect of sports image information
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