Распознавание подстилающей поверхности земли с помощью сверточной нейронной сети на одноплатном микрокомпьютере

The article presents development results for hardware and software system (micromodule), which detects and classifies underlying surface images of Earth. Given device has size 5.2×7.4×3.1 cm, mass 52 g and uses convolutional neural network based on MobileNetV2 architecture for image classification. The micromodule can be installed on board of a small spacecraft or a light unmanned aerial vehicle (drone). The information provided in this paper could be useful for engineers and researchers who are developing compact budget mobile systems for processing, analyzing and recognition of images

Сравнительный анализ вычислительных платформ для бортового микромодуля предварительного распознавания изображений

This paper is devoted to the analysis of basic hardware and software of recent cheap and commercially available computing microplatforms for selecting an appropriate solution for development of an onboard micromodule for preliminary recognition and selection of images of underlying surface of given types. It is assumed that the corresponding versions of the micromodule can be installed on board of small spacecraft or light unmanned aerial vehicles (drones). In this paper we consider a variant of a micromodule for drones. When choosing a microplatform, the main limitations were its low weight (no more than 300 g, including camera and interface equipment) and its relatively high performance (time for frame processing of a color image 320×240 pixels is no more than 300 ms). Another important limitation was the low price and commercial availability of microplatform on the Belarusian market. The information provided in this paper could be useful for engineers and researchers who are developing compact budget mobile systems for processing, analyzing and recognition of images.Приводятся результаты анализа базового аппаратного и программного обеспечения существующих недорогих и коммерчески доступных вычислительных микроплатформ с целью выбора подходящего решения при разработке бортового микромодуля предварительного распознавания и отбора изображений подстилающих поверхностей заданных типов. Предполагается, что соответствующие варианты микромодуля могут быть установлены на борту малых космических аппаратов либо легких беспилотных летательных аппаратов (дронов). Рассматривается вариант микромодуля для дронов. Основным ограничением при выборе микроплатформы был ее небольшой вес (не более 300 г, включая камеру и интерфейсное оборудование) при относительно высокой производительности (время распознавания кадра цветного изображения размером 320×240 пикселов не более 300 мс). Другими важными ограничениями являлись невысокая цена и коммерческая доступность микроплатформы на рынке Беларуси. Приведенные в работе сведения могут быть полезны инженерам и научным работникам, разрабатывающим компактные бюджетные мобильные системы обработки, анализа и распознавания изображений

Распознавание подстилающей поверхности Земли с помощью сверточной нейронной сети на одноплатном микрокомпьютере

The results of the development of hardware and software system (micromodule), which detects and classifies underlying surface images of the Earth are presented. The micromodule can be installed on board of a light unmanned aerial vehicle (drone). The device has the size 5.2×7.4×3.1 cm, the weight52 g, runs on a Raspberry Pi Zero Wireless single-board microcomputer and uses a convolutional neural network based on MobileNetV2 architecture for real-time image classification. When developing the micromodule, the authors aimed to achieve a real-time image classification on inexpensive mobile equipment with low computing power so that the classification quality is  comparable  to  popular  deep  convolutional  network  architectures. The provided information could be useful for engineers and researchers who are developing compact budget mobile systems for processing, analyzing and recognition of images.Приводятся результаты разработки программно-аппаратного комплекса (микромодуля) по обнаружению и классификации изображений подстилающей поверхности Земли. Микромодуль используется на борту легких беспилотных летательных аппаратов (дронов). Полученное устройство имеет размеры 5,2×7,4×3,1 см, массу 52 г., работает на одноплатном микрокомпьютере модели Raspberry Pi Zero Wireless и использует сверточную нейронную сеть на основе архитектуры MobileNetV2 для классификации изображений в реальном времени. При разработке микромодуля авторы преследовали цель добиться качества  классификации  изображений  в  реальном  времени  на  недорогом  мобильном  оборудовании с малой вычислительной способностью, сопоставимого с качеством классификации популярными архитектурами глубоких сверточных сетей. Приведенные в статье сведения могут быть полезны инженерам и научным работникам, разрабатывающим компактные бюджетные мобильные системы обработки, анализа и распознавания изображений

Comparative analysis of budget computing platforms for a portable micromodule of on-board image

This paper is devoted to the analysis of basic hardware and software of recent cheap and commercially available computing microplatforms for selecting an appropriate solution for development of an onboard micromodule for preliminary classification and selection of images of underlying surface of given types. It is assumed that the corresponding versions of the micromodule can be installed on board of small spacecraft or light unmanned aerial vehicles (drones). In this paper we consider a variant of a micromodule for drones. When choosing a microplatform, the main limitations were its low weight (no more than 300 grams, including camera and interface equipment) and its relatively high performance (time for frame processing of a color image 320Ч240 pixels is no more than 300 milliseconds). Another important limitation was the low price and commercial availability of micro-platform on the Belarusian market. The information provided in this paper could be useful for engineers and researchers who develop compact budget mobile systems for processing, analyzing and classification of images

Comparative analysis of computing platforms for onboard micromodule of provisional image recognition

This paper is devoted to the analysis of basic hardware and software of recent cheap and commercially available computing microplatforms for selecting an appropriate solution for development of an onboard micromodule for preliminary recognition and selection of images of underlying surface of given types. It is assumed that the corresponding versions of the micromodule can be installed on board of small spacecraft or light unmanned aerial vehicles (drones). In this paper we consider a variant of a micromodule for drones. When choosing a microplatform, the main limitations were its low weight (no more than 300 g, including camera and interface equipment) and its relatively high performance (time for frame processing of a color image 320×240 pixels is no more than 300 ms). Another important limitation was the low price and commercial availability of microplatform on the Belarusian market. The information provided in this paper could be useful for engineers and researchers who are developing compact budget mobile systems for processing, analyzing and recognition of images

Recognition of underlying surface using a convolutional neural network on a single-board computer

The results of the development of hardware and software system (micromodule), which detects and classifies underlying surface images of the Earth are presented. The micromodule can be installed on board of a light unmanned aerial vehicle (drone). The device has the size 5.2×7.4×3.1 cm, the weight52 g, runs on a Raspberry Pi Zero Wireless single-board microcomputer and uses a convolutional neural network based on MobileNetV2 architecture for real-time image classification. When developing the micromodule, the authors aimed to achieve a real-time image classification on inexpensive mobile equipment with low computing power so that the classification quality is  comparable  to  popular  deep  convolutional  network  architectures. The provided information could be useful for engineers and researchers who are developing compact budget mobile systems for processing, analyzing and recognition of images

Communication Management in Social Networks for the Actualization of Publications in the World Scientific Community on the Example of the Network Researchgate

Development of social networks of scientists in the World Wide Web creates new schemes for wider awareness of the global scientists' community of scientific research findings. In this case, existing information technologies are facing difficulties in resolving contradictions, generated by a broad stream of scientific publications and complexity of access to these publications. Resolution of this controversy is carried out thanks to "digitalization" of scientific content, which predetermines possibility of implementation of new principles for information disseminating, such as SMM (Social Media Marketing).To substantiate and assess SMM, we accepted the hypothesis about possibility of phenomenological presentation of lifecycle of scientific publications with the states of readers' community: S1 – unawareness; S2 –awareness; S3 – positive attitude; S4 – citation; S5 – negative attitude. In view of these states, the model of publication lifecycle based on a Markov chain was constructed. It was proposed to use SMM principles from professional marketing agencies in relation to promotion of scientific content on the Internet. A distinctive feature of this approach is that proposed Markov chain is adjusted to different possible states of reader's community on assessment of publication by establishing the values of transition probabilities, which are chosen for particular states based on the expert evaluation.We investigated the influence of expansion of readers' audience, provision of presentation clarity, articles uniqueness, professional orientation, and data objectivity on the distribution of publication readership. Effectiveness of publications promotion with an active authors' participation to follow up on their publications in social scientific networks was shown