Investigation of convolutional neural networks for object detection in aerospace images

Vladimir Skripachev, Mikhail Guida, Nikolay Guida, Alexander Zhukov


The article discusses current algorithms for solving problems of object recognition in images, their main features and advantages. A brief analysis of existing models of working with images based on convolutional neural networks is carried out. A brief overview of the features of convolutional neural network architectures, quantitative indicators for assessing the quality of their functioning and the types of tasks to be solved, the main features of working with images and the main emerging difficulties are considered, the features of processing aerospace images are highlighted. The problem of object recognition in aerospace images is formulated by adapting existing relevant algorithms and their combinations. The main problems of processing aerospace images and approaches to their solution, the application of established methods of object recognition in conventional images to the problems of object recognition in aerospace images are shown. The analysis of various neural network architectures in the prism of solving object recognition problems in aerospace images is carried out. Conclusions are drawn regarding the most successful combinations of various algorithms in the structure of neural networks when recognizing objects in aerospace images. The main factors that make it difficult to recognize objects in aerospace images and the directions of work to reduce their impact on the accuracy of neural networks when recognizing objects in aerospace images are determined.

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