Research on the technology of news video layout analysis based on lightweight neural network

The news video contains a lot of effective information, and the top task of further analyzing the news is to conduct layout analysis. It has very important research value in the field of news video analysis. The news media is an important medium for spreading important information, and the information spread by the news media has a significant impact on ordinary people. At present, all countries are actively developing their own news media to grasp the initiative of news. Layout analysis and recognition technology based on the deep learning is becoming more and more perfect. Compared with the traditional rule-based layout analysis algorithm, the layout analysis algorithm based on deep learning can filter redundant or useless data features, thereby obtaining and utilizing information better. In this paper, We innovatively propose NLNet –a noval neural architecture for news video layout analysis that is resource-efficient yet generalizable and scalable, formulated the category standards of news video layout elements, and proposed a news image layout analysis dataset called NewsLayout, containing 20000 news images. The whole architecture adopts a lightweight backbone network design, uses a lighter parameter, less computation, and more reasonable structure of the lightweight design to ensure accuracy and recall rate, improve the inference speed of the network.