scholarly journals Devising an image processing method for transport infrastructure monitoring systems

2021 ◽  
Vol 4 (2(112)) ◽  
pp. 18-25
Author(s):  
Oleksandr Volkov ◽  
Mykola Komar ◽  
Dmytro Volosheniuk
Keyword(s):  
Unmanned Aerial Vehicle ◽  
The State ◽  
Transport Infrastructure ◽  
Dynamic Monitoring ◽  
Contour Analysis ◽  
Binary Images ◽  
Infrastructure Monitoring ◽  
Sorting Data ◽  
Aerial Vehicle ◽  
Image Contour

Identifying and categorizing contours in images is important in many areas of computer vision. Examples include such operational tasks solved by using unmanned aerial vehicles as dynamic monitoring of the condition of transport infrastructure, in particular road markings. This study has established that current methods of image contour analysis do not produce clear and reliable results when solving the task of monitoring the state of road markings. Therefore, it is a relevant scientific and applied task to improve the methods and models of filtration, processing of binary images, and qualitative and meaningful separation of the boundaries of objects of interest. To solve the task of highlighting road marking contours on images acquired from an unmanned aerial vehicle, a method has been devised that includes an operational tool for image preprocessing – a combined filter. The method has several advantages and eliminates the limitations of known methods in determining the boundaries of the location of the object of interest, by highlighting the contours of a cluster of points using histograms. The method and procedures reported here make it possible to successfully solve problems that are largely similar to those that an expert person can face when solving intelligent tasks of processing and filtering information. The proposed method was verified at an enterprise producing the Ukrainian unmanned aerial vehicle "Spectator" during tests of information technology of dynamic monitoring of the state of transport infrastructure. The results could be implemented in promising intelligent control systems in the field of modeling human conscious behavior when sorting data required for the perception of environmental features

scholarly journals Identifying the Branch of Kiwifruit Based on Unmanned Aerial Vehicle (UAV) Images Using Deep Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4442
Author(s):  
Zijie Niu ◽  
Juntao Deng ◽  
Xu Zhang ◽  
Jun Zhang ◽  
Shijia Pan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Semantic Segmentation ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Distribution Maps ◽  
Time Operation ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Segmentation Image

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

Unmanned Aerial Systems Platform Research Prognosis

2012 ◽  
Vol 225 ◽  
pp. 555-560
Author(s):  
Javaan Chahl
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Systems Engineering ◽  
The State ◽  
Unmanned Aerial Systems ◽  
Research Opportunities ◽  
Engineering Approach ◽  
Current Trends ◽  
Research Investment ◽  
Aerial Vehicle ◽  
Aerial Systems

Much of aerospace academia is anticipating a boom in Unmanned Aerial Vehicle (UAV) funding and research opportunities. The expectation is built on the premise that UAVs will revolutionize aerospace, which is likely based on current trends. There is also an anticipation of an increasing number of new platforms and research investment, which is likely but must be analyzed carefully to determine where the opportunities might lie. This paper draws on the state of industry and a systems engineering approach. We explore what aspects of UAVs really are the results of aerospace science advances and what aspects will be rather more mundane works of engineering.

State Feedback Controller - An Alternative Approach Applied to Unmanned Aerial Vehicle

2014 ◽  
Vol 704 ◽  
pp. 270-276
Author(s):  
Renato A. Aguiar ◽  
Fabrizio Leonardi
Keyword(s):  
Control Systems ◽  
Unmanned Aerial Vehicle ◽  
State Feedback ◽  
The State ◽  
Feedback Controller ◽  
Feedback Gain ◽  
State Feedback Controller ◽  
Alternative Approach ◽  
Aerial Vehicle ◽  
Alternative Methodology

The primary goal of this work is to propose an alternative methodology as a first approach in the design of control systems by means of a feedback state gain. The proposed method is detailed and an application is presented. The results show relevant aspects regarding the state feedback gain, especially in regard to variation in the parameters of the plant.

scholarly journals Remote estimation of leaf area index (LAI) with unmanned aerial vehicle (UAV) imaging for different rice cultivars throughout the entire growing season

2021 ◽  
Author(s):  
Yan Gong ◽  
Kali Yang ◽  
Zhiheng Lin ◽  
Shenghui Fang ◽  
Xianting Wu ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Large Scale ◽  
Growing Season ◽  
Canopy Height ◽  
Dynamic Monitoring ◽  
Estimation Accuracy ◽  
Rice Cultivars ◽  
Simple Method ◽  
Area Index ◽  
Aerial Vehicle

Abstract Background: Rice is one of the most important grain crops worldwide. The accurate and dynamic monitoring of leaf are index (LAI) provides important information to evaluate rice growth and production. Methods: This study explores a simple method to remotely estimate LAI with Unmanned Aerial Vehicle (UAV) imaging for a variety of rice cultivars throughout the entire growing season. 48 different rice cultivars were planted in the study site and field campaigns were conducted once a week. For each campaign, several widely used vegetation indices (VI) were calculated from canopy reflectance obtained by 12-band UAV images, canopy height was derived from UAV RGB images and LAI was destructively measured by plant sampling. Results: The results showed the correlation of VI and LAI in rice throughout the entire growing season was weak, and for all tested indices there existed significant hysteresis of VI vs. LAI relationship between rice pre-heading and post-heading stages. The model based on the product of VI and canopy height could reduce such hysteresis and estimate rice LAI of the whole season with estimation errors under 24%, not requiring algorithm re-parameterization for different phenology stages. Conclusions: The progressing phenology can affect VI vs. LAI relationship in crops, especially for rice having quite different canopy spectra and structure after its panicle exsertion. Thus the models solely using VI to estimate rice LAI are phenology-specific and have high uncertainties for post-heading stages. The model developed in this study combines both remotely sensed canopy height and VI information, considerably improving rice LAI estimation accuracy at both pre- and post-heading stages. This method can be easily and efficiently implemented in UAV platforms for various rice cultivars during the entire growing season with no rice phenology and cultivar pre-knowledge, which has great potential for assisting rice breeding and field management studies at a large scale.

scholarly journals Partitioning complex tasks into subtasks in the process of planning goal-seeking behavior of an integral unmanned aerial vehicle in the state space

2020 ◽  
Author(s):  
В.Б. Мелехин ◽  
М.В. Хачумов
Keyword(s):  
Knowledge Representation ◽  
State Space ◽  
Unmanned Aerial Vehicle ◽  
Vision System ◽  
A Priori ◽  
The State ◽  
Operating Conditions ◽  
Complex Tasks ◽  
Seeking Behavior ◽  
Aerial Vehicle

Обозначены основные проблемы, связанные с разработкой интеллектуального решателя задач автоматической системы управления целенаправленным поведением интегрального беспилотного летательного аппарата в наземной проблемной среде. Рассмотрена конструкция типовых элементов представления знаний безотносительно к конкретной предметной области, позволяющих автономному беспилотному летальному аппарату, оснащенному манипулятором и системой технического зрения, решать сложные задачи в априори неописанных проблемных средах. Разработаны инструментальные средства разбиения сложных задач на подзадачи, обеспечивающие эффективный поиск их решения в пространстве состояний на основе типовых элементов представления знаний. Сформулированы основные методические положения, позволяющие синтезировать процедуры планирования целенаправленного поведения интегрального беспилотного летательного аппарата в сложных априори неописанных условиях функционирования. The main problems associated with the development of an intelligent problem solver of an automatic control system for the goal-seeking behavior of an integral unmanned aerial vehicle in a terrestrial problem environment are identified. The design of the typical elements of knowledge representation irrelative to the specific subject area is considered, allowing an autonomous unmanned aerial vehicle equipped with a manipulator and a vision system to solve complex problems in a priori undescribed problem environments. Tools are developed for partitioning complex tasks into sub-tasks, which provide an effective search for their solutions in the state space based on typical elements of knowledge representation. The main methodological provisions are formulated that allow synthesizing the planning procedures for the goal-seeking behavior of an integral unmanned aerial vehicle in a priori undescribed complex operating conditions.

Development of hardware and software complex for monitoring fires by unmanned aerial vehicles

2021 ◽  
pp. 118-129
Author(s):  
Вера Васильевна Извозчикова ◽  
Владимир Михайлович Шардаков ◽  
Вероника Вячеславовна Запорожко
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Processing System ◽  
Fire Detection ◽  
Dynamic Monitoring ◽  
Fire Source ◽  
Aerial Vehicles ◽  
Hardware Complex ◽  
Aerial Vehicle ◽  
Software And Hardware

Рассматривается вопрос обнаружения пожара с помощью беспилотного летательного аппарата (БПЛА) и разработанного программного обеспечения. Для раннего обнаружения пожара в нефтяных и газовых скважинах предложен алгоритм, основанный на применении цветовой модели RGB к полученным видеоизображениям от квадрокоптера. Приведены требования к БПЛА, смоделирован прототип программно-аппаратного комплекса дистанционного динамического мониторинга, включающего бортовую систему обработки информации БПЛА и информационную систему. Результаты проведенных экспериментов показали способность предложенного алгоритма успешно обнаруживать пожары на местности. Созданный программно-аппаратный комплекс позволит оперативно разрабатывать и принимать наиболее оптимальные решения по направлению пожарных расчетов и пожарной техники к местам возгорания, что особо актуально для отдаленных районов The paper addresses the problem of fire detection that is based on information obtained by an unmanned aerial vehicle. The purpose of this work is the possibility of early detection of ignition in oil and gas wells. An algorithm for fire detection based on the application of the RGB color model to the obtained video images of the studied area is proposed. The algorithm is based on the methods of spatial image segmentation and color quantization. According to the presented algorithm, a quadcopter transmits the incoming image from the digital video camera to the terminal, scanning the monitoring zone and GPS coordinates set by the operator. The algorithm for detecting the fire source is divided into four stages: analysis of the color intensity on the frame; checking the color of the area specified by the operator for coincidence with the range of fire; determining the fire coverage area in a certain territory and analyzing the change in the shape of the fire center relative to the angle of the moving unmanned aerial vehicle; determining the direction of fire propagation. Accurate automated determination of coordinates is carried out using the GPS signal of the fire, which allows starting localization and eliminating the fire source in a timely manner, thereby preventing a negative impact on people, nature and wildlife, as well as reducing the damage caused by the fire. A prototype of a software and hardware complex for remote dynamic monitoring, including an on-board information processing system for an unmanned aerial vehicle (UAV) and an information system, has been modelled. The paper presents the requirements for unmanned aerial vehicles, as well as analysis for the cost of the quadcopter’s flight time. The results of the experiments have shown the ability of the algorithm proposed by the authors to successfully detect the source of a fire on the ground. The created software and hardware complex allows quickly developing and making the most optimal decisions on the direction of fire crews and fire equipment to the fire sites, which is especially important for remote areas

scholarly journals Determination of the State of Strain of Large Floating Covers Using Unmanned Aerial Vehicle (UAV) Aided Photogrammetry

Sensors ◽  
2017 ◽  
Vol 17 (8) ◽  
pp. 1731 ◽  
Author(s):  
Wern Ong ◽  
Wing Chiu ◽  
Thomas Kuen ◽  
Jayantha Kodikara
Keyword(s):  
Unmanned Aerial Vehicle ◽  
The State ◽  
Aerial Vehicle

Unmanned aerial vehicle technology to assess the state of threatened biogenic formations: The vermetid reefs of mediterranean intertidal rocky coasts

2021 ◽  
Vol 251 ◽  
pp. 107228
Author(s):  
Luigia Donnarumma ◽  
Antonio D'Argenio ◽  
Roberto Sandulli ◽  
Giovanni Fulvio Russo ◽  
Renato Chemello
Keyword(s):  
Unmanned Aerial Vehicle ◽  
The State ◽  
Aerial Vehicle ◽  
Vehicle Technology ◽  
Rocky Coasts

scholarly journals On the question of the main elements of precision agriculture in Kostanay region on the example of LLP “Agricultural Experimental Station “Zarechnoye”

2021 ◽  
Vol 211 (08) ◽  
pp. 11-17
Author(s):  
D. Zhamalova ◽  
Marat Tashmuhamedov
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Aerial Photography ◽  
Crop Production ◽  
Near Infrared ◽  
Agricultural Land ◽  
The State ◽  
Technical Program ◽  
Aerial Vehicle ◽  
Program Transfer

Abstract. The purpose of the research is to analyze the quality of sowing operations (flaws, sifting), the completeness of seedlings based on multispectral images. The research was carried out in accordance with the purpose of implementing the scientific and technical program “Transfer and adaptation of precision farming technologies in the production of crop production on the principle of "demonstration farms (landfills)” in Kostanay region" in 2019. Methods. To perform monitoring work, an unmanned aerial vehicle of an airplane type was used; a multispectral (MS) camera equipped with sensors of the main channels. Agrotechnical requirements have been developed taking into account the data of the electronic map of fields and the specifics of the region. The analysis of the state of crops using an information and analytical resource was carried out. Results. A survey of agricultural crops was conducted in order to obtain data on the state of the fields by an unmanned aerial vehicle. Aerial photography was performed with the Make sense Red-Edge multispectral camera at an altitude of 300 meters. The survey was carried out over 19 fields in five spectral ranges: blue, green, red, extreme red, near infrared. Aerial photography data are the initial data for the construction of orthophotoplanes, digital surface models, 3D-models. After conducting a flyby of the territory, the general condition of agricultural land was analyzed. Measurements are made on the reference fields using a portable device – an N-tester. The scientific novelty lies in the fact that aerial photography of spring wheat, which is at the stage of 3–4 leaves, was carried out, which revealed changes in the NDVI value, which during the ground survey confirmed an increase in the degree of clogging by annual millet weeds of the selected areas.

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