scholarly journals Comparisons of feature extraction algorithm based on unmanned aerial vehicle image

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 472-478 ◽  
Author(s):  
Wenfei Xi ◽  
Zhengtao Shi ◽  
Dongsheng Li
Keyword(s):  
Feature Extraction ◽  
Unmanned Aerial Vehicle ◽  
Practical Case ◽  
Extraction Technology ◽  
Diverse Environment ◽  
Extraction Algorithm ◽  
Aerial Vehicle ◽  
Uav Image ◽  
Forstner Operator ◽  
Speed And Accuracy

AbstractFeature point extraction technology has become a research hotspot in the photogrammetry and computer vision. The commonly used point feature extraction operators are SIFT operator, Forstner operator, Harris operator and Moravec operator, etc. With the high spatial resolution characteristics, UAV image is different from the traditional aviation image. Based on these characteristics of the unmanned aerial vehicle (UAV), this paper uses several operators referred above to extract feature points from the building images, grassland images, shrubbery images, and vegetable greenhouses images. Through the practical case analysis, the performance, advantages, disadvantages and adaptability of each algorithm are compared and analyzed by considering their speed and accuracy. Finally, the suggestions of how to adapt different algorithms in diverse environment are proposed.

A three-step synthetic extraction algorithm for transmission lines

2019 ◽  
Vol 233 (17) ◽  
pp. 6218-6228
Author(s):  
Dongsheng Zhang ◽  
Jing Wang ◽  
Rongjuan Han ◽  
Aifen Tian
Keyword(s):  
Feature Extraction ◽  
Transmission Line ◽  
Unmanned Aerial Vehicle ◽  
Transmission Lines ◽  
Detection Efficiency ◽  
Step Method ◽  
Sobel Operator ◽  
Extraction Algorithm ◽  
Aerial Vehicle ◽  
Line Crossing

The combination of vision and unmanned aerial vehicle is an economic and effective solution for transmission line inspection. The key in the process of transmission line inspection is real-time extraction and recognition of transmission lines regardless of the complicated background. The horizontal and vertical disturbances are numerous in the complicated natural background. At the same time, transmission line crossing and different shooting angles of unmanned aerial vehicle lead to the phenomenon of overlapping lines, which makes it more difficult to extract and track transmission lines. Therefore, a three-step synthetic extraction algorithm for transmission lines is proposed in the article. Firstly, in view of the edge extraction problem of the image, a complementary edge feature extraction algorithm combining the Sobel operator with the Log operator is proposed to solve the problems such as discontinuous and uneven edges and low-detection efficiency caused by the general Canny operator for edge detection. Secondly, in view of the line feature extraction, a coarse-fine directional optimization algorithm is proposed. The horizontal and vertical interferences of the extraction of transmission lines under complex background and the problem of line crossing in the image are eliminated. The algorithm has higher detection effect and efficiency than Hough transform. Finally, a similarity determination mechanism is proposed to merge the adjacent lines, which avoids the loss of the tracking line caused by overlapping lines in the image. A quadrotor unmanned aerial vehicle is used to carry out the experiment of transmission line detection in the article. The experimental results demonstrate that the three-step method of line extraction is more effective and efficient than the current algorithms.

scholarly journals Super Resolution pada Citra Udara menggunakan Convolutional Neural Network

2021 ◽  
Vol 9 (1) ◽  
pp. 71
Author(s):  
MUHAMMAD EFAN ABDULFATTAH ◽  
LEDYA NOVAMIZANTI ◽  
SYAMSUL RIZAL
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Unmanned Aerial Vehicle ◽  
Super Resolution ◽  
Image Features ◽  
Scale Factor ◽  
Aerial Imagery ◽  
Aerial Vehicle

ABSTRAKBencana di Indonesia didominasi oleh bencana hidrometeorologi yang mengakibatkan kerusakan dalam skala besar. Melalui pemetaan, penanganan yang menyeluruh dapat dilakukan guna membantu analisa dan penindakan selanjutnya. Unmanned Aerial Vehicle (UAV) dapat digunakan sebagai alat bantu pemetaan dari udara. Namun, karena faktor kamera maupun perangkat pengolah citra yang tidak memenuhi spesifikasi, hasilnya menjadi kurang informatif. Penelitian ini mengusulkan Super Resolution pada citra udara berbasis Convolutional Neural Network (CNN) dengan model DCSCN. Model terdiri atas Feature Extraction Network untuk mengekstraksi ciri citra, dan Reconstruction Network untuk merekonstruksi citra. Performa DCSCN dibandingkan dengan Super Resolution CNN (SRCNN). Eksperimen dilakukan pada dataset Set5 dengan nilai scale factor 2, 3 dan 4. Secara berurutan SRCNN menghasilkan nilai PSNR dan SSIM sebesar 36.66 dB / 0.9542, 32.75 dB / 0.9090 dan 30.49 dB / 0.8628. Performa DCSCN meningkat menjadi 37.614dB / 0.9588, 33.86 dB / 0.9225 dan 31.48 dB / 0.8851.Kata kunci: citra udara, deep learning, super resolution ABSTRACTDisasters in Indonesia are dominated by hydrometeorological disasters, which cause large-scale damage. Through mapping, comprehensive handling can be done to help the analysis and subsequent action. Unmanned Aerial Vehicle (UAV) can be used as an aerial mapping tool. However, due to the camera and image processing devices that do not meet specifications, the results are less informative. This research proposes Super Resolution on aerial imagery based on Convolutional Neural Network (CNN) with the DCSCN model. The model consists of Feature Extraction Network for extracting image features and Reconstruction Network for reconstructing images. DCSCN's performance is compared to CNN Super Resolution (SRCNN). Experiments were carried out on the Set5 dataset with scale factor values 2, 3, and 4. The SRCNN sequentially produced PSNR and SSIM values of 36.66dB / 0.9542, 32.75dB / 0.9090 and 30.49dB / 0.8628. DCSCN's performance increased to 37,614dB / 0.9588, 33.86dB / 0.9225 and 31.48dB / 0.8851.Keywords: aerial imagery, deep learning, super resolution

The Extraction of Wheat Lodging Area in UAV’s Image Used Spectral and Texture Features

2014 ◽  
Vol 651-653 ◽  
pp. 2390-2393 ◽  
Author(s):  
Hai Ying Liu ◽  
Gui Jun Yang ◽  
Hong Chun Zhu
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Scientific Research ◽  
Object Oriented ◽  
Texture Features ◽  
Important Application ◽  
Extraction Technology ◽  
Research Results ◽  
Aerial Vehicle ◽  
First Time

Wheat lodging makes great effects on the output and subsequent production, so we need to know the situation of wheat lodging at the first time quickly and efficiently. The characters of Unmanned Aerial Vehicle remote sensing just meet the demands. Firstly, the spectral and texture features are analyzed, and the area of wheat lodging is extracted using methods of object-oriented and the extraction is analyzed and compared. The research results of this paper is the successful practice of Agricultural scientific research and application by using Unmanned Aerial Vehicle remote sensing and Object-oriented extraction technology, so it have important application value and meaningful.

Subsurface targets detection using Faster R-CNN for Unmanned Aerial Vehicle Magnetic Survey

2020 ◽  
Author(s):  
Yaoxin Zheng ◽  
Xiaojuan Zhang ◽  
Yaxin Mu ◽  
Wupeng Xie
Keyword(s):  
Feature Extraction ◽  
Unmanned Aerial Vehicle ◽  
Data Augmentation ◽  
Field Tests ◽  
Magnetic Survey ◽  
Positional Error ◽  
Buried Objects ◽  
Object Proposal ◽  
Proposal A ◽  
Aerial Vehicle

<p>Unmanned Aerial Vehicle (UAV) has become a viable platform for magnetic surveys, but the interference generated during flight and lack of the interpretation method for survey data limits its application. In this paper, we present a structure of a half-fixed boom for the UAV-magnetometer system. Compared to suspend the magnetometer on a long rope or cable, our new structure reduces interference and positional error meanwhile increases flight stability. The interference field was removed through compensation based on leveling, with root mean square error significantly reduced from 2.7889 nT to 0.2809 nT. The Faster R-CNN network was adapted for the detection of subsurface buried objects (i.e. Unexploded Ordnance) in UAV magnetic surveys, our Faster R-CNN object detection network is composed of a feature extraction network followed by two subnetworks, the feature extraction network we use is a pre-trained CNN called ResNet-50, the first subnetwork is a region proposal network (RPN) and the second subnetwork is trained to predict the actual class of each object proposal. A labeled dataset that contains 740 images was used for training and each image contains one or more labeled instances of mag anomaly, data augmentation is used by randomly flipping the image and associated box labels horizontally to improve network accuracy, the trained object detector was evaluated on both simulated and field test images. All implementations in this work were accomplished through MATLAB Deep Learning Toolbox using a PC with a GPU compute capability 7.5. Preliminary results reveal that the proposed technique can automatically confirm the number of subsurface targets, in the meantime results from different field tests show its robustness. Significant improvements have made compared to traditional computer vision methods and hence become quite promising to be applied in the field of UAV magnetic survey.</p>

scholarly journals Historical Building Inspection using the Unmanned Aerial Vehicle (Uav)

2020 ◽  
Vol 11 (3) ◽  
Author(s):  
Hanita Yusof ◽  
◽  
Mustaffa Anjang Ahmad ◽  
Aadam Mohammed Taha Abdullah ◽  
◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Visual Inspection ◽  
Suitable Condition ◽  
Historical Building ◽  
The People ◽  
Aerial Vehicle ◽  
Difficult Area ◽  
Uav Image ◽  
Building Condition ◽  
Survey Protocol

Building inspection is very much required for all buildings especially those historical ones to maintain the structure condition and the safety of the people around. Visual inspection is commonly conducted using manual descriptive information carried out by the inspector personally. The problem with this technique of assessment is that the time needed to write all the defect description on site and to access difficult area can be hazardous. The aim of performing historical building inspection on Tan Swee Hoe’s historical mansion is to evaluate the overall condition of the building with Unmanned Aerial Vehicle (UAV) image assisted inspection and Condition Survey Protocol 1 (CSP1) method. From this drone assisted inspection, it shows that the time spent on site is less than half an hour and the data collected being evaluated with CSP1 method defined that the building is dilapidated. The overall building condition is in red class and require serious attention to avoid any injuries to the visitors. To prevent a possible failure of a building in the coming years, a suitable condition inspection has been carried out to identify the current existing defect so that it would be fixed before further damage to the building and visitors around it.

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