Recognition of aerial photography objects based on data sets with different aggregation of classes
Keywords:recognition of aerial photography objects, classification of data sets, recognition accuracy, neural network of the ConvNets group
The object of this work is the recognition algorithms of aerial photography objects, namely, the analysis of recognition accuracy based on data sets with different aggregation classes.
To solve this problem, an information system for object recognition based on aerial photography data has been developed. An architecture based on neural network architectures of the ConvNets group with structural modifications was chosen and used to create the information system. The use of a convolutional neural network of the ConvNets group in the architecture of the information system for the recognition of objects of aerial photography gives high accuracy rates when training the information system and validating its results. But the authors did not find any studies on the learning of the neural network of the ConvNets group. Therefore, it was decided to conduct an analysis in which case the ConvNets network will provide validation results with higher accuracy when the training takes place on datasets with or without class aggregation.
The authors performed an analysis of the accuracy of recognition of aerial photography objects based on data sets with different aggregation classes. The dataset used for neural network training consisted of 3-channel labeled images of 64x64 pixels size. Based on the analysis, the optimal number of epochs for training is selected, which makes it possible to recognize aerial photography objects with greater accuracy and speed. It was concluded that greater accuracy in image classification is achieved for sampling without crossing data from different classes (without aggregation of classes). The result of the work is recommended for use in the automation of dataset filling and information filtering of visual images
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Copyright (c) 2023 Pylyp Prystavka, Kseniia Dukhnovska, Oksana Kovtun, Olga Leshchenko, Olha Cholyshkina, Vadym Semenov
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