Implementation convolution neural network model method for solve real time objects detection task with geospatial  approach use

А. А. Махмутов

Информационные технологии интеллектуальной поддержки принятия решений, Информационные технологии интеллектуальной поддержки принятия решений 2018

Implementation convolution neural network model method for solve real time objects detection task with geospatial approach use

А. А. Махмутов

Изменена: 2018-06-20

Аннотация

In this paper, considering the implementation of the model of a convolutional neural network for solving real-time object detection problems using the approach of geoinformation technologies for processing and mapping of detected objects as geospatial units. Also this article covers issues related to the peculiarities of training, software and hardware implementation of the model of the convolutional neural network. The relevance of this article is to identify the behavior of detectable objects in different conditions on the investigated spatial sections using the convolutional network as deep learning method, therefore, it allows identifying predicative models, the use of which is necessary for solving the problems for urbanized entities infrastructure control.

Ключевые слова

Real Time Nearest Remote Sensing; Deep Learning; Convolutional Neural Network; GIS; Machine Learning; Recognition; Object Detection

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