Performance Analysis in Object Diameter Estimation based on Single and Multiple Non-Destructive and Discrete Infrared Sensors

2014 ◽  
Vol 67 (1) ◽  
Author(s):  
Norashikin M. Thamrin ◽  
Norhashim Mohd. Arshad ◽  
Ramli Adnan ◽  
Rosidah Sam ◽  
Noorfazdli Abd. Razak ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Laser Scanning ◽  
Pass Rate ◽  
Small Scale ◽  
Infrared Sensors ◽  
Scanning Method ◽  
Average Percentage ◽  
Multiple Sensors ◽  
Localization And Mapping ◽  
Aerial Vehicle

In Simultaneous Localization and Mapping (SLAM) technique, recognizing and marking the landmarks in the environment is very important. Therefore, in a commercial farm, rows of trees, borderline of rows as well as the trees and other features are mostly used by the researchers in realizing the automation process in this field. In this paper, the detection of the tree based on its diameter is focused. There are few techniques available in determining the size of the tree trunk inclusive of the laser scanning method as well as image-based measurements. However, those techniques require heavy computations and equipments which become constraints in a lightweight unmanned aerial vehicle implementation. Therefore, in this paper, the detection of an object by using a single and multiple infrared sensors on a non-stationary automated vehicle platform is discussed. The experiments were executed on different size of objects in order to investigate the effectiveness of this proposed method. This work is initially tested on the ground, based in the lab environment by using an omni directional vehicle which later will be adapted on a small-scale unmanned aerial vehicle implementation for tree diameter estimation in the agriculture farm.  In the current study, comparing multiple sensors with single sensor orientation showed that the average percentage of the pass rate in the pole recognition for the former is relatively more accurate than the latter with 93.2 percent and 74.2 percent, respectively. 

scholarly journals Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

2020 ◽  
Vol 13 (1) ◽  
pp. 24
Author(s):  
Yuanshuo Hao ◽  
Faris Rafi Almay Widagdo ◽  
Xin Liu ◽  
Ying Quan ◽  
Lihu Dong ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Random Effects ◽  
Forest Inventory ◽  
Point Cloud ◽  
Laser Scanning ◽  
Small Scale ◽  
Individual Tree ◽  
Scanning Angle ◽  
Tree Diameter ◽  
Aerial Vehicle

Unmanned aerial vehicle laser scanning (UAVLS) systems present a relatively new means of remote sensing and are increasingly applied in the field of forest ecology and management. However, one of the most essential parameters in forest inventory, tree diameter at breast height (DBH), cannot be directly extracted from aerial point cloud data due to the limitations of scanning angle and canopy obstruction. Therefore, in this study DBH-UAVLS point cloud estimation models were established using a generalized nonlinear mixed-effects (NLME) model. The experiments were conducted using Larix olgensis as the subject species, and a total of 8364 correctly delineated trees from UAVLS data within 118 plots across 11 sites were used for DBH modeling. Both tree- and plot-level metrics were obtained using light detection and ranging (LiDAR) and were used as the models’ independent predictors. The results indicated that the addition of site-level random effects significantly improved the model fitting. Compared with nonparametric modeling approaches (random forest and k-nearest neighbors) and uni- or multivariable weighted nonlinear least square regression through leave-one-site-out cross-validation, the NLME model with local calibration achieved the lowest root mean square error (RMSE) values (1.94 cm) and the most stable prediction across different sites. Using the site in a random-effects model improved the transferability of LiDAR-based DBH estimation. The best linear unbiased predictor (BLUP), used to conduct local model calibration, led to an improvement in the models’ performance as the number of field measurements increased. The research provides a baseline for unmanned aerial vehicle (UAV) small-scale forest inventories and might be a reasonable alternative for operational forestry.

scholarly journals ACCURACY OF SNOW DEPTH ESTIMATION IN MOUNTAIN AND PRAIRIE ENVIRONMENTS BY AN UNMANNED AERIAL VEHICLE

2016 ◽  
Author(s):  
Phillip Harder ◽  
Michael Schirmer ◽  
John Pomeroy ◽  
Warren Helgason
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Snow Depth ◽  
Laser Scanning ◽  
Low Cost ◽  
Depth Estimation ◽  
Snow Accumulation ◽  
Small Scale ◽  
Canadian Prairie ◽  
Aerial Vehicle ◽  
Mean Square Errors

Abstract. The quantification of the spatial distribution of snow is crucial to predict and assess snow as a water resource and understand land-atmosphere interactions in cold regions. Typical remote sensing approaches to quantify snow depth have focused on terrestrial and airborne laser scanning and recently airborne (manned and unmanned) photogrammetry. In this study photography from a small unmanned aerial vehicle (UAV) was used to generate digital surface models (DSMs) and orthomosaics for snowcovers at a cultivated agricultural Canadian Prairie and a sparsely-vegetated Rocky Mountain alpine ridgetop site using Structure from Motion (SfM). The ability of this method to quantify snow depth, changes in depth and its spatial variability was assessed for different terrain types over time. Root mean square errors in snow depth estimation from the DSMs were 8.8 cm for a short prairie grain stubble surface, 13.7 cm for a tall prairie grain stubble surface and 8.5 cm for an alpine mountain surface. This technique provided meaningful information on maximum snow accumulation and snow-covered area depletion at all sites, while temporal changes in snow depth could also be quantified at the alpine site due to the deeper snowpack and consequent higher signal-to noise-ratio. The application of SfM to UAV photographs can estimate snow depth in areas with snow depth > 30 cm – this restricts its utility for studies of the ablation of shallow, windblown snowpacks. Accuracy varied with surface characteristics, sunlight and wind speed during the flight, with the most consistent performance found for wind speeds < 6 m s−1, clear skies, high sun angles and surfaces with negligible vegetation cover. Relative to surfaces having greater contrast and more identifiable features, snow surfaces present unique challenges when applying SfM to imagery collected by a small UAV for the generation of DSMs. Regardless, the low cost, deployment mobility and the capability of repeat-on-demand flights that generate DSMs and orthomosaics of unprecedented spatial resolution provide exciting opportunities to quantify previously unobservable small-scale variability in snow depth and its dynamics.

scholarly journals Comparison of Coniferous Plantation Heights Using Unmanned Aerial Vehicle (UAV) Laser Scanning and Stereo Photogrammetry

2021 ◽  
Vol 13 (15) ◽  
pp. 2885
Author(s):  
Mei Li ◽  
Zengyuan Li ◽  
Qingwang Liu ◽  
Erxue Chen
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Prediction Accuracy ◽  
Laser Scanning ◽  
Forest Stand ◽  
Coniferous Plantation ◽  
Point Density ◽  
Stereo Photogrammetry ◽  
Aerial Vehicle ◽  
Stand Height ◽  
Median Height

Plantation forests play a critical role in forest products and ecosystems. Unmanned aerial vehicle (UAV) remote sensing has become a promising technology in forest related applications. The stand heights will reflect the growth and competition of individual trees in plantation. UAV laser scanning (ULS) and UAV stereo photogrammetry (USP) can both be used to estimate stand heights using different algorithms. Thus, this study aimed to deeply explore the variations of four kinds of stand heights including mean height, Lorey’s height, dominated height, and median height of coniferous plantations using different models based on ULS and USP data. In addition, the impacts of thinned point density of 30 pts to 10 pts, 5 pts, 1 pts, and 0.8 pts/m2 were also analyzed. Forest stand heights were estimated from ULS and USP data metrics by linear regression and the prediction accuracy was assessed by 10-fold cross validation. The results showed that the prediction accuracy of the stand heights using metrics from USP was basically as good as that of ULS. Lorey’s height had the highest prediction accuracy, followed by dominated height, mean height, and median height. The correlation between height percentiles metrics from ULS and USP increased with the increased height. Different stand heights had their corresponding best height percentiles as variables based on stand height characteristics. Furthermore, canopy height model (CHM)-based metrics performed slightly better than normalized point cloud (NPC)-based metrics. The USP was not able to extract exact terrain information in a continuous coniferous plantation for forest canopy cover (CC) over 0.49. The combination of USP and terrain from ULS can be used to estimate forest stand heights with high accuracy. In addition, the estimation accuracy of each forest stand height was slightly affected by point density, which can also be ignored.

scholarly journals Deep learning–based fifth-generation millimeter-wave communication channel tracking for unmanned aerial vehicle Internet of things networks

2019 ◽  
Vol 15 (8) ◽  
pp. 155014771986588 ◽  
Author(s):  
Shan Meng ◽  
Xin Dai ◽  
Bicheng Xiao ◽  
Yimin Zhou ◽  
Yumei Li ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Unmanned Aerial Vehicle ◽  
Millimeter Wave ◽  
Estimation Method ◽  
Attitude Estimation ◽  
Base Stations ◽  
Communication Link ◽  
Multiple Sensors ◽  
Channel Tracking ◽  
Aerial Vehicle

Using unmanned aerial vehicle as movable base stations is a promising approach to enhance network coverage. Moreover, movable unmanned aerial vehicle–base stations can dynamically move to the target devices to expand the communication range as relays in the scenario of the Internet of things. In this article, we consider a communication system with movable unmanned aerial vehicle–base stations in millimeter-Wave. The movable unmanned aerial vehicle–base stations are equipped with antennas and multiple sensors for channel tracking. The cylindrical array antenna is mounted on the movable unmanned aerial vehicle–movable base stations, making the beam omnidirectional. Furthermore, the attitude estimation method using the deep neural network can replace the traditional attitude estimation method. The estimated unmanned aerial vehicle attitude information is combined with beamforming technology to realize a reliable communication link. Simulation experiments have been performed, and the results have verified the effectiveness of the proposed method.

scholarly journals UAV-YOLO: Small Object Detection on Unmanned Aerial Vehicle Perspective

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2238 ◽  
Author(s):  
Mingjie Liu ◽  
Xianhao Wang ◽  
Anjian Zhou ◽  
Xiuyuan Fu ◽  
Yiwei Ma ◽  
...  
Keyword(s):  
Object Detection ◽  
Unmanned Aerial Vehicle ◽  
Spatial Information ◽  
Detection Method ◽  
Small Scale ◽  
Small Object ◽  
Aerial Vehicle ◽  
Public Dataset ◽  
Small Object Detection ◽  
Level Performance

Object detection, as a fundamental task in computer vision, has been developed enormously, but is still challenging work, especially for Unmanned Aerial Vehicle (UAV) perspective due to small scale of the target. In this study, the authors develop a special detection method for small objects in UAV perspective. Based on YOLOv3, the Resblock in darknet is first optimized by concatenating two ResNet units that have the same width and height. Then, the entire darknet structure is improved by increasing convolution operation at an early layer to enrich spatial information. Both these two optimizations can enlarge the receptive filed. Furthermore, UAV-viewed dataset is collected to UAV perspective or small object detection. An optimized training method is also proposed based on collected UAV-viewed dataset. The experimental results on public dataset and our collected UAV-viewed dataset show distinct performance improvement on small object detection with keeping the same level performance on normal dataset, which means our proposed method adapts to different kinds of conditions.

Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

2020 ◽  
Vol 50 (10) ◽  
pp. 1012-1024
Author(s):  
Meimei Wang ◽  
Jiayuan Lin
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Laser Scanning ◽  
Tree Height ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Surface Model ◽  
Individual Tree ◽  
Aerial Vehicle ◽  
The Mean

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. Object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

scholarly journals Three-Dimensional Digital Documentation of Cultural Heritage Site Based on the Convergence of Terrestrial Laser Scanning and Unmanned Aerial Vehicle Photogrammetry

2019 ◽  
Vol 8 (2) ◽  
pp. 53 ◽  
Author(s):  
Young Jo ◽  
Seonghyuk Hong
Keyword(s):  
Cultural Heritage ◽  
Unmanned Aerial Vehicle ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Terrestrial Laser Scanning ◽  
Three Dimensional Model ◽  
Positional Accuracy ◽  
Heritage Sites ◽  
Aerial Vehicle ◽  
Digital Documentation

Three-dimensional digital technology is important in the maintenance and monitoring of cultural heritage sites. This study focuses on using a combination of terrestrial laser scanning and unmanned aerial vehicle (UAV) photogrammetry to establish a three-dimensional model and the associated digital documentation of the Magoksa Temple, Republic of Korea. Herein, terrestrial laser scanning and UAV photogrammetry was used to acquire the perpendicular geometry of the buildings and sites, where UAV photogrammetry yielded higher planar data acquisition rate in upper zones, such as the roof of a building, than terrestrial laser scanning. On comparing the two technologies’ accuracy based on their ground control points, laser scanning was observed to provide higher positional accuracy than photogrammetry. The overall discrepancy between the two technologies was found to be sufficient for the generation of convergent data. Thus, the terrestrial laser scanning and UAV photogrammetry data were aligned and merged post conversion into compatible extensions. A three-dimensional (3D) model, with planar and perpendicular geometries, based on the hybrid data-point cloud was developed. This study demonstrates the potential for using the integration of terrestrial laser scanning and UAV photogrammetry in 3D digital documentation and spatial analysis of cultural heritage sites.

Designing an Unmanned Aerial Vehicle for Specific Aerial Applications of Insecticides and Herbicides

2016 ◽  
Author(s):  
Mohammed S. Mayeed ◽  
Gabriel Darveau
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Lift Force ◽  
State Of The Art ◽  
Low Cost ◽  
Von Mises Stress ◽  
Small Scale ◽  
Aerial Application ◽  
Bottom Line ◽  
Flight Duration ◽  
Aerial Vehicle

In this study a gasoline powered hexa-copter unmanned aerial vehicle (UAV) has been designed as a solution to farmers’ need for a low cost, easy to maintain, long flight duration, and multi-purpose means of specific aerial applications for insecticides and herbicides. Application of herbicides and pesticides by airplane is an example of how farmers have used technology to improve their bottom line and overall quality of life. Fields can now be sprayed in under an hour instead of consuming an entire day. However, if a producer has noxious weeds in only a small area, fixed-wing aerial application cannot be used as it is only accurate enough to do an entire field. Currently there is no solution for small scale, accurate, aerial herbicide application to meet this need. The currently available Yamaha Rmax UAV costs a tremendous amount of money and also requires a lot of money to maintain. Though it may be useful in large scale aerial spraying on the farm land, it would not be used in targeted specific areas as it is not efficient in specific applications. The gasoline powered hexacopter UAV designed in this study is a low cost solution to farmers’ need for specific aerial applications of insecticides and herbicides. The UAV design can carry 2–3 gallons of herbicide (16.7–25.0 lbs.) for a flight time of more than 30 minutes without refueling. The design could be transported in a 60.3in × 56.7in pickup bed. Structural and fatigue analyses are performed on the complete structure using state of the art software SolidWorks Simulation. The minimum factor of safety is obtained to be 10 based on maximum von Mises stress failure criteria. Under normal conditions with an estimated commercial use of 100 cycles per day it is observed that the design would survive for about 13 years without any fatigue failure. A drop test analysis is performed to ensure the design can survive a 5 feet freefall and a frequency analysis is also performed to observe the critical natural frequency of the structure. Flow simulations are performed on the 6 propellers/blades model using state of the art software SolidWorks Flow Simulation to observe the effect of vorticity interactions on the lift force. The design has been reasonably optimized based on maximizing the lift force. With this new UAV design small scale and substantial farmers could afford a personal UAV for aerial applications with a small amount of capital whose absence hindered efficient and effective specific aerial application for many years.

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