Assessment and Inventory of Palms in a Plantation by Template Matching of Unmanned Aerial Vehicle (UAV) Image

2016 ◽  
Vol 11 (2) ◽  
pp. 1-8
Author(s):  
O Popoola ◽  
A Salami ◽  
K Adepoju
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Template Matching ◽  
Aerial Vehicle ◽  
Uav Image

scholarly journals Research on Method of Farmland Obstacle Boundary Extraction in UAV Remote Sensing Images

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4431
Author(s):  
Fang ◽  
Chen ◽  
Jiang ◽  
Wang ◽  
Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Correlation Coefficient ◽  
Unmanned Aerial Vehicle ◽  
Information Acquisition ◽  
Template Matching ◽  
Obstacle Detection ◽  
Image Resolution ◽  
Average Deviation ◽  
Actual Measurement ◽  
Aerial Vehicle

Aimed at the problem of obstacle detection in farmland, the research proposed to adopt the method of farmland information acquisition based on unmanned aerial vehicle landmark image, and improved the method of extracting obstacle boundary based on standard correlation coefficient template matching and assessed the influence of different image resolutions on the precision of obstacle extraction. Analyzing the RGB image of farmland acquired by unmanned aerial vehicle remote sensing technology, this research got the following results. Firstly, we applied a method automatically registering coordinates, and the average deviations on the X and Y direction were 4.6 cm and 12.0 cm respectively, while the average deviations manually by ArcGIS were 4.6 cm and 5.7 cm. Secondly, with an improvement on the step of the traditional correlation coefficient template matching, we reduced the time of template matching from 12.2 s to 4.6 s. The average deviation between edge length of obstacles calculated by corner points extracted by the algorithm and that by actual measurement was 4.0 cm. Lastly, by compressing the original image on a different ratio, when the pixel reached 735 × 2174 (the image resolution reached 6 cm), the obstacle boundary was extracted based on correlation coefficient template matching, the average deviations of boundary points I of six obstacles on the X and Y were respectively 0.87 and 0.95 cm, and the whole process of detection took about 3.1 s. To sum up, it can be concluded that the algorithm of automatically registered coordinates and of automatically extracted obstacle boundary, which were designed in this research, can be applied to the establishment of a basic information collection system for navigation in future study. The best image pixel of obstacle boundary detection proposed after integrating the detection precision and detection time can be the theoretical basis for deciding the unmanned aerial vehicle remote sensing image resolution.

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.

scholarly journals Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection

2021 ◽  
Vol 13 (21) ◽  
pp. 4377
Author(s):  
Long Sun ◽  
Jie Chen ◽  
Dazheng Feng ◽  
Mengdao Xing
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Target Detection ◽  
Template Matching ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Information Warfare ◽  
Aerial Vehicle ◽  
Uav Images

Unmanned aerial vehicle (UAV) is one of the main means of information warfare, such as in battlefield cruises, reconnaissance, and military strikes. Rapid detection and accurate recognition of key targets in UAV images are the basis of subsequent military tasks. The UAV image has characteristics of high resolution and small target size, and in practical application, the detection speed is often required to be fast. Existing algorithms are not able to achieve an effective trade-off between detection accuracy and speed. Therefore, this paper proposes a parallel ensemble deep learning framework for unmanned aerial vehicle video multi-target detection, which is a global and local joint detection strategy. It combines a deep learning target detection algorithm with template matching to make full use of image information. It also integrates multi-process and multi-threading mechanisms to speed up processing. Experiments show that the system has high detection accuracy for targets with focal lengths varying from one to ten times. At the same time, the real-time and stable display of detection results is realized by aiming at the moving UAV video image.

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.

scholarly journals Unmanned aerial vehicle (UAV) image haze removal using dark channel prior

2019 ◽  
Vol 1324 ◽  
pp. 012036 ◽  
Author(s):  
Jimin Yu ◽  
Yaoheng Wang ◽  
Shangbo Zhou ◽  
Rumeng Zhai ◽  
Saiao Huang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Dark Channel Prior ◽  
Haze Removal ◽  
Dark Channel ◽  
Aerial Vehicle ◽  
Uav Image

scholarly journals Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning

2019 ◽  
Vol 11 (6) ◽  
pp. 643 ◽  
Author(s):  
Anastasiia Safonova ◽  
Siham Tabik ◽  
Domingo Alcaraz-Segura ◽  
Alexey Rubtsov ◽  
Yuriy Maglinets ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Bark Beetle ◽  
Forest Health ◽  
Tree Damage ◽  
Abies Sibirica ◽  
Polygraphus Proximus ◽  
State Nature Reserve ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Uav Image

Invasion of the Polygraphus proximus Blandford bark beetle causes catastrophic damage to forests with firs (Abies sibirica Ledeb) in Russia, especially in Central Siberia. Determining tree damage stage based on the shape, texture and colour of tree crown in unmanned aerial vehicle (UAV) images could help to assess forest health in a faster and cheaper way. However, this task is challenging since (i) fir trees at different damage stages coexist and overlap in the canopy, (ii) the distribution of fir trees in nature is irregular and hence distinguishing between different crowns is hard, even for the human eye. Motivated by the latest advances in computer vision and machine learning, this work proposes a two-stage solution: In a first stage, we built a detection strategy that finds the regions of the input UAV image that are more likely to contain a crown, in the second stage, we developed a new convolutional neural network (CNN) architecture that predicts the fir tree damage stage in each candidate region. Our experiments show that the proposed approach shows satisfactory results on UAV Red, Green, Blue (RGB) images of forest areas in the state nature reserve “Stolby” (Krasnoyarsk, Russia).

Sign in / Sign up

Export Citation Format

Share Document