SOLVING THE PROBLEM OF INTER-AIRCRAFT NAVIGATION TO ENSURE FLYING IN FORMATION USING A TECHNICAL VISION SYSTEM

2021 ◽  
pp. 32-39
Author(s):  
Д.А. Смирнов ◽  
В.Г. Бондарев ◽  
А.В. Николенко
Keyword(s):  
Image Processing ◽  
Flight Control ◽  
Vision System ◽  
Weather Conditions ◽  
Infrared Range ◽  
Light Sources ◽  
Navigation Systems ◽  
Aircraft Structure ◽  
Aircraft Navigation ◽  
Technical Vision

Проведен краткий анализ как отечественных, так и зарубежных систем межсамолетной навигации. В ходе анализа были отражены недостатки систем межсамолетной навигации и представлен актуальный подход повышения точности системы навигации за счет применения системы технического зрения. Для определения местоположения ведущего самолета предлагается рассмотреть в качестве измерительного комплекса систему технического зрения, которая способна решать большой круг задач на различных этапах, в частности, и полет строем. Систему технического зрения предлагается установить на ведомом самолете с целью измерения всех параметров, необходимых для формирования автоматического управления полетом летательного аппарата. Обработка изображений ведущего самолета выполняется с целью определения координат трех идентичных точек на фоточувствительных матрицах. Причем в качестве этих точек выбираются оптически контрастные элементы конструкции летательного аппарата, например окончания крыла, хвостового оперения и т.д. Для упрощения процедуры обработки изображений возможно использование полупроводниковых источников света в инфракрасном диапазоне (например, с длиной волны λ = 1,54 мкм), что позволяет работать даже в сложных метеоусловиях. Такой подход может быть использован при автоматизации полета строем более чем двух летательных аппаратов, при этом необходимо только оборудовать системой технического зрения все ведомые самолеты группы The article provides a brief analysis of both domestic and foreign inter-aircraft navigation systems. In the course of the analysis, we found the shortcomings of inter-aircraft navigation systems and presented an up-to-date approach to improving the accuracy of the navigation system through the use of a technical vision system. To determine the location of the leading aircraft, we proposed to consider a technical vision system as a measuring complex, which is able to solve a large range of tasks at various stages, in particular, flight in formation. We proposed to install the technical vision system on the slave aircraft in order to measure all the parameters necessary for the formation of automatic flight control of the aircraft. We performed an image processing of the leading aircraft to determine the coordinates of three identical points on photosensitive matrices. Moreover, we selected optically contrasting elements of the aircraft structure as these points, for example, the end of the wing, tail, etc. To simplify the image processing procedure, it is possible to use semiconductor light sources in the infrared range (for example, with a wavelength of λ = 1.54 microns), which allows us to work even in difficult weather conditions. This approach can be used when automating a flight in formation of more than two aircraft, while it is only necessary to equip all the guided aircraft of the group with a technical vision system

Technical Vision Model of the Visual Systems for Industry Application

2021 ◽  
pp. 288-310
Author(s):  
Oleg Sytnik ◽  
Vladimir Kartashov
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Visual System ◽  
Mathematical Models ◽  
Human Visual System ◽  
Vision System ◽  
Vision Systems ◽  
Industrial Systems ◽  
Visual Systems ◽  
Technical Vision

The problems of highlighting the main informational aspects of images and creating their adequate models are discussed in the chapter. Vision systems can receive information about an object in different frequency ranges and in a form that is not accessible to the human visual system. Vision systems distort the information contained in the image. Therefore, to create effective image processing and transmission systems, it is necessary to formulate mathematical models of signals and interference. The chapter discusses the features of perception by the human visual system and the issues of harmonizing the technical characteristics of industrial systems for receiving and transmitting images. Methods and algorithms of pattern recognition are discussed. The problem of conjugation of the characteristics of the technical vision system with the consumer of information is considered.

SOLVING THE PROBLEM OF DETERMINING THE LOCATION OF AN UNMANNED AERIAL VEHICLE IN THE COORDINATE COUNTING MODE USING A TECHNICAL VISION SYSTEM

2021 ◽  
pp. 43-49
Author(s):  
Д.А. Смирнов ◽  
В.Г. Бондарев ◽  
А.В. Тепловодский ◽  
А.В. Николенко
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vision System ◽  
High Accuracy ◽  
Measuring Instruments ◽  
Navigation Systems ◽  
Optoelectronic System ◽  
Technical Vision ◽  
Surface Image ◽  
Aerial Vehicle ◽  
Special Points

Представлено обоснование использования оптико-электронной системы в качестве навигационно-измерительного комплекса. Проведен краткий анализ существующих систем навигации, применимых для беспилотного летательного аппарата, и предложен алгоритм обеспечения системы видеонаблюдения в режиме счисления координат с помощью системы технического зрения. Задачу счисления координат БЛА с использованием видеопоследовательностей изображений земной поверхности можно решить с высокой точностью с помощью бинокулярной СТЗ. Однако в случае выхода из строя одной из камер определение координат местоположения будет продолжаться с достаточной точностью для решения поставленной задачи. А недостаток измерительных средств обеспечивается за счет использования 6 особых точек земной поверхности. Поэтому предложен алгоритм определения местоположения с помощью монокулярной системы технического зрения. Для решения задачи определения местоположения выделяются и определяются координаты особых точек на изображении поверхности. Для нахождения особых точек была выполнена обработка оцифрованного изображения методом FAST-9. Так как изображение получается цветным, то процедура нахождения особых точек является надежным путем применения метода FAST-9 для двух или даже трех цветовых компонент. Данная процедура позволяет достигнуть высокой точности определения счисляемых координат БЛА. Для решения задач счисления координат предпочтительно использование методов простых итераций, Брауна или Ньютона We present the rationale for the use of an optoelectronic system as a navigation-measuring complex. We carried out a brief analysis of existing navigation systems applicable to an unmanned aerial vehicle and propose an algorithm for providing a video surveillance system in the reckoning mode using a vision system. The problem of reckoning UAV coordinates using video sequences of images of the earth's surface can be solved with high accuracy using a binocular TVS. However, in case of failure of one of the cameras, the determination of the coordinates of the location will continue with sufficient accuracy to solve the task. And the lack of measuring instruments is ensured through the use of 6 special points of the earth's surface. Therefore, we propose an algorithm for determining the location using a monocular vision system. To solve the problem of determining the location, we selected and determined the coordinates of the singular points on the surface image. To find the special points, we processed the digitized image using the FAST-9 method. Since the image is obtained in color, the procedure for finding special points is reliable by applying the FAST-9 method for two or even three color components. This procedure allows you to achieve high accuracy in determining the reckoning coordinates of the UAV. To solve problems of reckoning coordinates, it is preferable to use the methods of simple iterations, Brown or Newton

scholarly journals Mathematical modelling of an airborne technical vision system development

2020 ◽  
Vol 224 ◽  
pp. 02025
Author(s):  
A.I. Novikov ◽  
E.R. Muratov ◽  
M.B. Nikiforov ◽  
D.A. Kolchaev
Keyword(s):  
Image Processing ◽  
Mathematical Modelling ◽  
Image Fusion ◽  
Information Technologies ◽  
Vision System ◽  
Geometric Transformation ◽  
Processing Unit ◽  
Technical Vision ◽  
Wide Range ◽  
High Level

An airborne technical vision system of a modern aircraft is, on the one hand, a complex of multispectral sensors and, on the other, airborne computer equipment. The onboard computer solves a wide range of complex digital image processing tasks. The strict requirements are imposed on the computer mathematical software on the image processing time and on the quality of the result displayed on the pilot screen. The software development for such a complex system involves mathematical modelling and use modern information technologies in process of computational algorithms development for task solving. The proposed work discusses ways of solving subtasks of one of the most important subsystems of the airborne complex - the image fusion subsystem and solving the inverse navigation problem. The subsystem is represented by two blocks of tasks: tasks of primary image processing and high-level tasks. The primary processing unit solves the problems of image noise suppression, image enhancement, image fusion and edge detection. In the high-level block of tasks, the tasks of key points detection on real and virtual images, the tasks of a geometric transformation estimation of one image to the plane of another, and the task of a 3D model construction of the underlying surface are solved. The paper presents the results of mathematical modelling in the process of algorithms development for above problems solving. The new results of mathematical modelling of the noise estimation problem are presented in detail. The description of methods and results of other problems solving is less detailed due to article limit but provided with links to the corresponding publications of the authors.

Modern Technology for Image processing and Computer vision -A Review

2018 ◽  
Vol 1 (2) ◽  
pp. 17-23
Author(s):  
Takialddin Al Smadi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Video Compression ◽  
Video Processing ◽  
Vision System ◽  
Citrus Fruit ◽  
Modern Technology ◽  
Image And Video Processing ◽  
Depth Video

This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality.   © 2018 JASET, International Scholars and Researchers Association

scholarly journals Restoring Raindrops Using Attentive Generative Adversarial Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 7034
Author(s):  
Hee-Deok Yang
Keyword(s):  
Weather Conditions ◽  
Recurrent Network ◽  
Generative Adversarial Networks ◽  
Navigation Systems ◽  
Vision Systems ◽  
Generative Adversarial Network ◽  
Network Layers ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Outdoor Vision

Artificial intelligence technologies and vision systems are used in various devices, such as automotive navigation systems, object-tracking systems, and intelligent closed-circuit televisions. In particular, outdoor vision systems have been applied across numerous fields of analysis. Despite their widespread use, current systems work well under good weather conditions. They cannot account for inclement conditions, such as rain, fog, mist, and snow. Images captured under inclement conditions degrade the performance of vision systems. Vision systems need to detect, recognize, and remove noise because of rain, snow, and mist to boost the performance of the algorithms employed in image processing. Several studies have targeted the removal of noise resulting from inclement conditions. We focused on eliminating the effects of raindrops on images captured with outdoor vision systems in which the camera was exposed to rain. An attentive generative adversarial network (ATTGAN) was used to remove raindrops from the images. This network was composed of two parts: an attentive-recurrent network and a contextual autoencoder. The ATTGAN generated an attention map to detect rain droplets. A de-rained image was generated by increasing the number of attentive-recurrent network layers. We increased the number of visual attentive-recurrent network layers in order to prevent gradient sparsity so that the entire generation was more stable against the network without preventing the network from converging. The experimental results confirmed that the extended ATTGAN could effectively remove various types of raindrops from images.

scholarly journals An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture

2022 ◽  
pp. 1-20
Author(s):  
Amin Basiri ◽  
Valerio Mariani ◽  
Giuseppe Silano ◽  
Muhammad Aatif ◽  
Luigi Iannelli ◽  
...  
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Flight Control ◽  
Precision Agriculture ◽  
Navigation Systems ◽  
Agricultural Crops ◽  
Cluttered Environments ◽  
Main Motivation ◽  
Autonomous Guidance ◽  
Planning Problems

Abstract Multi-rotor Unmanned Aerial Vehicles (UAVs), although originally designed and developed for defence and military purposes, in the last ten years have gained momentum, especially for civilian applications, such as search and rescue, surveying and mapping, and agricultural crops and monitoring. Thanks to their hovering and Vertical Take-Off and Landing (VTOL) capabilities and the capacity to carry out tasks with complete autonomy, they are now a standard platform for both research and industrial uses. However, while the flight control architecture is well established in the literature, there are still many challenges in designing autonomous guidance and navigation systems to make the UAV able to work in constrained and cluttered environments or also indoors. Therefore, the main motivation of this work is to provide a comprehensive and exhaustive literature review on the numerous methods and approaches to address path-planning problems for multi-rotor UAVs. In particular, the inclusion of a review of the related research in the context of Precision Agriculture (PA) provides a unified and accessible presentation for researchers who are initiating their endeavours in this subject.

Vehicle Security Distance Control Based on Binocular Parallax Vision

2014 ◽  
Vol 644-650 ◽  
pp. 207-210
Author(s):  
Shuang Liu ◽  
Xiang Jie Kong ◽  
Ming Cai Shan
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
High Precision ◽  
Stereo Matching ◽  
Vision System ◽  
Intelligent Transportation ◽  
Distance Control ◽  
Calibration Methods ◽  
Computer Vision Technology ◽  
Vehicle Security

Binocular parallax vision system is a kind of computer vision technology. Two cameras on different locations can get two different pictures of same object. The space position of the object can be calculated by the parallax information of two different pictures. The binocular parallax vision technology includes cameras calibration, image processing, and stereo matching analysis. The paper will introduce the inside and outside parameters calibration methods, and combing the traffic applications, designed the calibrating scheme. The parameters that obtained according to the scheme can meet the demands of measuring the vehicle distance. The high precision can meet the needs of intelligent transportation vehicles in a security vehicles spacing survey, which is an effective way for measuring the front car distance.

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