Cloud Recognition Based on Lightweight Neural Network

Cloud type recognition is a basic task in the field of ground-based cloud meteorological observation. It is of great significance to identify cloud type accurately for improving the accuracy of weather forecast. In this paper, we propose a new lightweight convolutional neural network model, called LCCNet, to achieve accurate cloud recognition of ground-based cloud images. We build a standard ground-based cloud data set contains 11 categories, called HBMCD, which is recognized by professional meteorological stations and conforms to the standards of the World Meteorological organization. Compared with other existing ground-based cloud data sets, HBMCD have larger data volume, complete cloud categories and uniform quality, and are more professional and comprehensive. A number of comparative experiments demonstrates that the proposed LCCNet model has stronger characterization ability and higher classification accuracy, which is up to 97.35%. At the same time, its parameter amount and operation complexity are lower than the existing network models, which makes it possible for equipment integration and practical application.