Version 1
: Received: 23 November 2018 / Approved: 26 November 2018 / Online: 26 November 2018 (10:57:17 CET)
How to cite:
Rong, F.; Shasha, L.; Qingzheng, X.; Kun, L. A Detection algorithm based on Convolutional Neural Network. Preprints2018, 2018110583. https://doi.org/10.20944/preprints201811.0583.v1
Rong, F.; Shasha, L.; Qingzheng, X.; Kun, L. A Detection algorithm based on Convolutional Neural Network. Preprints 2018, 2018110583. https://doi.org/10.20944/preprints201811.0583.v1
Rong, F.; Shasha, L.; Qingzheng, X.; Kun, L. A Detection algorithm based on Convolutional Neural Network. Preprints2018, 2018110583. https://doi.org/10.20944/preprints201811.0583.v1
APA Style
Rong, F., Shasha, L., Qingzheng, X., & Kun, L. (2018). A Detection algorithm based on Convolutional Neural Network. Preprints. https://doi.org/10.20944/preprints201811.0583.v1
Chicago/Turabian Style
Rong, F., Xu Qingzheng and Liu Kun. 2018 "A Detection algorithm based on Convolutional Neural Network" Preprints. https://doi.org/10.20944/preprints201811.0583.v1
Abstract
The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.
Keywords
Station logo;Convolutional Neural Network; Detection
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
