scholarly journals APPLICATION OF MULTI-FACETED FUNCTION MODEL BASED ON VONDRAK FILTER OPTIMIZATION IN UAV AERIAL IMAGE HEIGHT CORRECTION

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1313-1317
Author(s):  
F. Zhou ◽  
L. Pu ◽  
S. H. Tang ◽  
Y. F. Yang
Keyword(s):  
High Precision ◽  
Surface Fitting ◽  
Aerial Image ◽  
Experimental Comparison ◽  
Cubic Surface ◽  
Aerial Photogrammetry ◽  
Function Fitting ◽  
Fitting Method ◽  
Elevation Correction ◽  
Vondrak Filter

Abstract. With the rapid development of drone technology and digital camera technology, the method of obtaining high-precision coordinates based on UAV aerial photogrammetry technology is popular. The plane coordinate accuracy of the aerial image of the drone has been able to meet the needs of practical applications, but the elevation accuracy is generally low. Aiming at the low elevation accuracy of UAV aerial photogrammetry, a multi-face function fitting method based on Vondrak filter optimization was proposed. The improved fitting model was used to obtain the elevation correction value of the aerial image, thereby obtaining high-precision image elevation data. In this paper, based on the traditional multi-face function fitting method, some known points were used to model and find the difference between the measured elevation value and the measured elevation. The Vondrak filter was used to smooth the fitting result. Finally, a small number of known elevation points were used for checking, so that the obtained elevation was compared with the actual elevation. The experimental comparison showed that the improved multi-face function fitting method used Vondrak filter was improved by 34.76% compared with the quadric surface fitting, and improved by 14.48% compared with the optimized cubic surface fitting method. Research shows that the multi-faceted function method based on Vondrak filtering is superior to the traditional elevation correction method. The experiment verifies the effectiveness and feasibility of the improved method, and provides some reference value for the research of aerial image elevation correction model.

Accurate Measurement and Digital Modelling of Complex Surface

2014 ◽  
Vol 670-671 ◽  
pp. 1264-1268 ◽  
Author(s):  
Yan Qiang Liu ◽  
Hong Xun Cheng ◽  
Yan Zhong Wang ◽  
Jian Shen Wang ◽  
Ya Peng Sun
Keyword(s):  
Neural Network ◽  
High Precision ◽  
Complex Surface ◽  
Surface Fitting ◽  
Nurbs Surface ◽  
Fitting Method ◽  
Digital Modelling

Accurate measurement and modeling of complex surface is the foundation to precisely evaluate the error of complex surface. According to the NURBS method for rebuilding complex surface, a sampling data automatic replenishment method based on neural network is proposed and the sampling data with high precision is acquired. By using bicubic NURBS surface fitting method, the complex surface is rebuilt precisely and efficiently.

scholarly journals Flux control coefficients determined by inhibitor titration: the design and analysis of experiments to minimize errors

1993 ◽  
Vol 296 (2) ◽  
pp. 423-433 ◽  
Author(s):  
J R Small
Keyword(s):  
Random Errors ◽  
Flux Control ◽  
Graph Method ◽  
Function Fitting ◽  
Fitting Method ◽  
Simple Extrapolation ◽  
Experimental Errors ◽  
Flux Control Coefficients ◽  
Fitted Function ◽  
Control Coefficients

This paper is a study into the effects of experimental error on the estimated values of flux control coefficients obtained using specific inhibitors. Two possible techniques for analysing the experimental data are compared: a simple extrapolation method (the so-called graph method) and a non-linear function fitting method. For these techniques, the sources of systematic errors are identified and the effects of systematic and random errors are quantified, using both statistical analysis and numerical computation. It is shown that the graph method is very sensitive to random errors and, under all conditions studied, that the fitting method, even under conditions where the assumptions underlying the fitted function do not hold, outperformed the graph method. Possible ways of designing experiments to minimize the effects of experimental errors are analysed and discussed.

HE-YOLO: Aerial Target Detection Based On Improved YOLOv3

2021 ◽  
Author(s):  
Yuqing Zhao ◽  
Jinlu Jia ◽  
Di Liu ◽  
Yurong Qian
Keyword(s):  
Target Detection ◽  
Loss Function ◽  
Detection Rate ◽  
Aerial Image ◽  
Experimental Comparison ◽  
Small Scale ◽  
Detection Accuracy ◽  
False Detection Rate ◽  
False Detection ◽  
Bounding Box

Aerial image-based target detection has problems such as low accuracy in multiscale target detection situations, slow detection speed, missed targets and falsely detected targets. To solve this problem, this paper proposes a detection algorithm based on the improved You Only Look Once (YOLO)v3 network architecture from the perspective of model efficiency and applies it to multiscale image-based target detection. First, the K-means clustering algorithm is used to cluster an aerial dataset and optimize the anchor frame parameters of the network to improve the effectiveness of target detection. Second, the feature extraction method of the algorithm is improved, and a feature fusion method is used to establish a multiscale (large-, medium-, and small-scale) prediction layer, which mitigates the problem of small target information loss in deep networks and improves the detection accuracy of the algorithm. Finally, label regularization processing is performed on the predicted value, the generalized intersection over union (GIoU) is used as the bounding box regression loss function, and the focal loss function is integrated into the bounding box confidence loss function, which not only improves the target detection accuracy but also effectively reduces the false detection rate and missed target rate of the algorithm. An experimental comparison on the RSOD and NWPU VHR-10 aerial datasets shows that the detection effect of high-efficiency YOLO (HE-YOLO) is significantly improved compared with that of YOLOv3, and the average detection accuracies are increased by 8.92% and 7.79% on the two datasets, respectively. The algorithm not only shows better detection performance for multiscale targets but also reduces the missed target rate and false detection rate and has good robustness and generalizability.

A new short-arc fitting method with high precision using Adam optimization algorithm

Optik ◽  
2020 ◽  
Vol 212 ◽  
pp. 164788
Author(s):  
Zhigen Fei ◽  
Zhiying Wu ◽  
Yanqiu Xiao ◽  
Jun Ma ◽  
Wenbin He
Keyword(s):  
High Precision ◽  
Optimization Algorithm ◽  
Fitting Method ◽  
Short Arc

scholarly journals Experimental Comparison of Outdoor Baseline Measurements by Different Methods

2019 ◽  
Vol 131 ◽  
pp. 01057
Author(s):  
Youyi Gu ◽  
Li Wang ◽  
Fengzhuo Xiang ◽  
Wen Ouyang ◽  
Lixing Jiang
Keyword(s):  
Direct Measurement ◽  
High Precision ◽  
Optical Measurement ◽  
Experimental Results ◽  
Experimental Comparison ◽  
Total Station ◽  
Length Standard ◽  
Gnss Receivers ◽  
Surveying And Mapping

Outdoor baseline is the special length standard in the field of surveying and mapping, it can be used to verify the addition and multiplication constants of the total station and other photoelectric rangefinders. In order to ensure the authenticity, accuracy and reliability of verification results, conducting outdoor baseline traceability periodically is essential. At present, direct measurement by 24m invar tape or high precision electro-optical measurement is mainly used to achieve the traceability of outdoor baseline in China. Based on Shenyang baseline field, high precision rangefinder μ-base, 24m invar tape and high precision GNSS receivers are used for comparison experiments, and the experimental results are analyzed.

A Study of the Peak Boundary Method and Effect Evaluation Based on NaI(TI) Detector for γ-Spectrum Analysis

2013 ◽  
Vol 347-350 ◽  
pp. 1006-1011
Author(s):  
Yao Zong Yang ◽  
Fang Fang ◽  
Jian Feng He ◽  
Jun Jun Ran
Keyword(s):  
Spectrum Analysis ◽  
Gaussian Function ◽  
Comparison Method ◽  
Convergence Result ◽  
Weighting Factor ◽  
Gamma Energy ◽  
Common Boundary ◽  
Function Fitting ◽  
Qualitative Detection ◽  
Fitting Method

In order to accurately analyze the result, which comes from quantitative and qualitative detection based on gamma energy spectrum of NaI(T1) detector, peak boundary has become one of the main factors which influence the spectrum analysis. By implementing some common boundary determining algorithms in the Matlab such as simple comparison method, derivative method, symmetry zero-area method, the full width method and gaussian function fitting method, as well as comparing effectiveness among those algorithm, priority of those boundary algorithms is evaluated. At the same time, because the boundary determining algorithm based on traditional gaussian fitting is not ideal, the new boundary determining algorithm based on the least squares fitting of gaussian function with weighting factor is proposed. The practice verifies that this method is stability and can obtain preferable convergence result in boundary determining of unimodal or combination peaks.

Estimation and Display of Triangular Mesh Curvature in Reverse Engineering

2010 ◽  
Vol 136 ◽  
pp. 95-102
Author(s):  
Hui Cun Shen ◽  
J.J. Nie ◽  
W.S. Zong ◽  
J. Wang ◽  
B.G. Yang
Keyword(s):  
Reverse Engineering ◽  
Surface Fitting ◽  
Triangular Mesh ◽  
Surface Curvature ◽  
Estimation Methods ◽  
Quadric Surface ◽  
Fitting Method ◽  
Curvature Estimation ◽  
Curvature Group ◽  
Paraboloid Surface

The estimation of triangular mesh curvature is implemented by establishing local quadric surface at vertexes of mesh. Deduction course of quadric surface curvature calculation is presented. Errors and complexity of two curvature estimation methods, which are the ecumenical quadric surface fitting method and the quadric paraboloid surface fitting method respectively, are compared. Technique for curvature group display is put forward. This technique can display features of mesh distinctly, even though the curvature values of mesh distribute non-uniformly in their variety range.

Voltage Sag Source Localization Based on the Voltage Distance Function in Distribution Network

2013 ◽  
Vol 391 ◽  
pp. 607-610 ◽  
Author(s):  
Yu Liu ◽  
Jin Hao Wang ◽  
Chao Ying Yang
Keyword(s):  
Least Squares ◽  
Source Localization ◽  
Distance Function ◽  
Distribution Network ◽  
Voltage Sag ◽  
Sorting Algorithm ◽  
Line Voltage ◽  
Function Fitting ◽  
Fitting Method ◽  
Bus Voltage

To realize voltage sag source localization in distribution network, the paper proposes a function fitting method based on the least squares. Establish a voltage distance function in response to fault distance changes by the line voltage. According to the voltage distance function, combine with the bus voltage after fault to find out likely fault section and distance. Through the sorting algorithm to sort all possible results, weaken the effect of pseudo fault point on the judgment result. Finally the simulation verifies the effectiveness of the method.

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