scholarly journals Monitoring Dynamic Deformation of Building Using Unmanned Aerial Vehicle

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yongquan Ge ◽  
Xianzhi Yu ◽  
Mingzhi Chen ◽  
Chengxin Yu ◽  
Yingchun Liu ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Dynamic Deformation ◽  
Motion Parallax ◽  
Steel Structure ◽  
Steel Structures ◽  
Deformation Monitoring ◽  
Dynamic Monitoring ◽  
Structure Deformation ◽  
Aerial Vehicle ◽  
Mp Method

The height irregularity and complexity of steel structures bring difficulties to dynamic deformation monitoring. PDMS (photogrammetric dynamic monitoring system) can obtain the dynamic deformation of the steel structure, but the flexibility of monitoring is limited because the camera station can only be placed on the ground. In this study, UAV (unmanned aerial vehicle) -PDMS is innovatively proposed to be used in monitoring dynamic deformation of steel structures, and it is verified in the steel frame test and Jinan Olympic Sports Center Tennis Stadium test. To solve the problem that the attitude of UAV cannot be strictly maintained in the hovering process, the improved Z-MP (zero-centered motion parallax) method is used, and the monitoring results are compared with the original Z-MP method. The feasibility of UAV-PDMS applied to steel structure deformation monitoring and the feasibility of improving the Z-MP method to reduce UAV hovering error are verified. The monitoring results showed that the steel structures of the Jinan Olympic Sports Center Tennis Stadium were robust, and the deformations were elastic and within the permissible value.

scholarly journals REAL-TIME MONITORING DEFORMATION OF BUILDING USING PHOTOGRAPHY DYNAMIC MONITORING SYSTEM

2021 ◽  
Vol 30 (1) ◽  
Author(s):  
Yongquan Ge ◽  
Chengxin Yu ◽  
Tonglong Zhao ◽  
Xiaodong Liu
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Dynamic Deformation ◽  
Motion Parallax ◽  
Deformation Monitoring ◽  
Dynamic Monitoring ◽  
Scale Transformation ◽  
Reference Image ◽  
Time Deformation ◽  
Mp Method

The spatial structure building is a type of building system; it is necessary to monitor deformation to determine its stability and robustness. Under the dynamic deformation of structures, it is challenging to determine appropriate zero image (the reference image) if we use the PST-IM- MP (photograph scale transformation-image matching-motion parallax) method to obtain the deformation of structures. This paper offers the Z-MP (zero-centered motion parallax) method to solve these problems and offers PDMS (Photography Dynamic Monitoring System) based on the digital photography system to monitor the dynamic deformation of the tennis stadium located in Jinan Olympic Sports Center. The results showed that the spatial structures of the tennis stadium were robust, and the deformations were elastic and within the permissible value. Compared with the PST-IM-MP method, the Z-MP method is more suitable for deformation monitoring structures under real-time deformation. This paper indicates PDMS has advantages of the simplicity of operations, automation, and the ability of non-contact dynamic deformation monitoring for multiple points in a short period. In the future, it will have broader application prospects.

scholarly journals A New Approach for Cylindrical Steel Structure Deformation Monitoring by Dense Point Clouds

2021 ◽  
Vol 13 (12) ◽  
pp. 2263
Author(s):  
Dongfeng Jia ◽  
Weiping Zhang ◽  
Yuhao Wang ◽  
Yanping Liu
Keyword(s):  
Steel Structure ◽  
Steel Structures ◽  
Point Clouds ◽  
Deformation Monitoring ◽  
Deformation Analysis ◽  
Relative Deformation ◽  
Structure Deformation ◽  
Single Time ◽  
Cylindrical Steel ◽  
Cylinder Fitting

As fundamental load-bearing parts, the cylindrical steel structures of transmission towers relate to the stability of the main structures in terms of topological relation and performance. Therefore, the periodic monitoring of a cylindrical steel structure is necessary to maintain the safety and stability of existing structures in energy transmission. Most studies on deformation analysis are still focused on the process of identifying discrepancies in the state of a structure by observing it at different times, yet relative deformation analysis based on the data acquired in single time has not been investigated effectively. In this study, the piecewise cylinder fitting method is presented to fit the point clouds collected at a single time to compute the relative inclination of a cylindrical steel structure. The standard deviation is adopted as a measure to evaluate the degree of structure deformation. Meanwhile, the inclination rate of each section is compared with the conventional method on the basis of the piecewise cylinder fitting parameters. The validity and accuracy of the algorithm are verified by real transmission tower point cloud data. Experimental results show that the piecewise cylinder fitting algorithm proposed in this research can meet the accuracy requirements of cylindrical steel structure deformation analysis and has high application value in the field of structure deformation monitoring.

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.

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 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

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

Application Analysis of Structure Deformation Monitoring Prediction Methods

2010 ◽  
Vol 163-167 ◽  
pp. 2674-2677
Author(s):  
Li Ting Zhang ◽  
Hao Zhang
Keyword(s):  
Prediction Models ◽  
Prediction Method ◽  
Information Model ◽  
Deformation Monitoring ◽  
Dynamic Monitoring ◽  
Prediction Methods ◽  
Structure Deformation ◽  
New Information ◽  
Application Analysis ◽  
Scientific Prediction

Nowadays structure construction and monitoring, hazard forecasting depends more and more on scientific prediction methods. This paper focus on prediction method, which based on GM(1,1) model of Gary Theory. With a set of dynamic monitoring data from one structure, we introduce the modeling method of old information model, new information model and equal dimension new information model. An analysis of these prediction models reliability is performed to compare these models. The result of these models can predict the structure settlement proved that a suitable prediction method could provide help to structure safety and reduce unnecessary lost.

scholarly journals SPACE-AIR-GROUND CO-OBSERVATION IN WATERSHED MANAGEMENT: THE ESTABLISHMENT OF SYSTEM

2018 ◽  
Vol XLII-3 ◽  
pp. 2465-2468 ◽  
Author(s):  
L. Zhong ◽  
J. Yu ◽  
X. Tang ◽  
S. Pan
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Unmanned Aerial Vehicle ◽  
Watershed Management ◽  
Information Acquisition ◽  
Dynamic Monitoring ◽  
Aerial Vehicle ◽  
Low Altitude ◽  
Water Administration

To realize real-time, detailed, and standardized watershed monitoring and management, a dynamic monitoring system is proposed, at all levels (space, air, and ground), by comprehensively utilizing advanced satellite and low-altitude unmanned aerial vehicle (UAV) technologies The system can be used to monitor and manage all kinds of sensitive water targets. This study takes water administration enforcement as an example for proving it feasibility by selecting typical study areas. This study shows that the proposed system is a promising information acquisition means, contributing to the development of watershed management.

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