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Rich feature hierarchies for accurate object detection and semantic segmentation

Formulation of the problem. Over the past few years, there has been little progress in object detection techniques. The most efficient are complex computational ensemble methods, which usually combine several low-level image properties with high-level properties. However, every day interest in artificial intelligence is growing, and the idea of using neural networks on board a spacecraft, with the possibility of making decisions and issuing one-time commands, is very promising, since it makes it possible to analyze a large data stream in real time without resorting to ground station, thereby not losing information when transmitting a packet. The purpose of the work is to conduct research on the possibility of effective use of modern models of neural networks, to develop a methodology for their use in the problem of object detection and analysis of the element base for hardware implementation with the possibility of using convolutional neural networks for thermovideotelemetry on board a spacecraft. Results of work. An approach has been formulated that combines two key ideas: 1) application of high-throughput convolutional neural networks for downward processing of image regions in order to localize and segment objects; 2) preliminary training for the auxiliary task, followed by fine tuning of the domain, which gives a significant increase in performance in the case when the training data is insufficient. The analysis of the element base for the possibility of hardware implementation of neural networks on board a spacecraft using electrical radio products of domestic and foreign production is carried out. Practical significance. The efficiency of preliminary network training for an auxiliary task is shown, followed by fine tuning of the subject area. A technique is described that makes it possible to increase the average accuracy of detecting objects in an image by more than 30%. The analysis of the existing element base, the possibility of hardware implementation of neural networks on board the spacecraft using electrical radio products of domestic and foreign production, as well as the criteria for selecting key elements.