Application of Data Augmentation Methods to Unmanned Aerial Vehicle Monitoring System for Facial Camouflage Recognition

2017 ◽  
pp. 190-197 ◽  
Author(s):  
Yanyang Li ◽  
Sanqing Hu ◽  
Wenhao Huang ◽  
Jianhai Zhang
Keyword(s):  
Monitoring System ◽  
Unmanned Aerial Vehicle ◽  
Data Augmentation ◽  
Vehicle Monitoring ◽  
Aerial Vehicle

scholarly journals Analysis of an Unmanned Aerial Vehicle Monitoring System for Resurveying of Shipping Routes

2016 ◽  
Vol 3 (1) ◽  
pp. 102-111
Author(s):  
Aleksandrs Urbahs ◽  
Rima Mickevičienė ◽  
Vasilij Djačkov ◽  
Kristīne Carjova ◽  
Valdas Jankūnas ◽  
...  
Keyword(s):  
Monitoring System ◽  
Unmanned Aerial Vehicle ◽  
Survey Methods ◽  
Environment Monitoring ◽  
Vehicle Monitoring ◽  
Hydrographic Survey ◽  
Survey System ◽  
Aerial Vehicle ◽  
Security Operations ◽  
Marine Safety

Abstract The paper gives brief description of the conventional and innovative hydrography survey methods and constraints connected with the realization. Proposed hydrographic survey system based on the use of Unmanned Aerial and Maritime systems provides functionality to conduct hydrographic measurements and environment monitoring. System can be easily adapted to fulfil marine safety and security operations, e.g. intrusion threat monitoring, hazardous pollutions monitoring and prevention operations, icing conditions monitoring.

scholarly journals Smart Environment Monitoring System using Unmanned Aerial Vehicle in Bangladesh

2018 ◽  
pp. 165995
Author(s):  
Md. Al-Farabi ◽  
Muntasir Chowdhury ◽  
Md. Readuzzaman ◽  
Md. Hossain ◽  
Saifur Sabuj ◽  
...  
Keyword(s):  
Monitoring System ◽  
Unmanned Aerial Vehicle ◽  
Smart Environment ◽  
Environment Monitoring ◽  
Aerial Vehicle

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 Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

Sensors ◽  
2017 ◽  
Vol 17 (3) ◽  
pp. 502 ◽  
Author(s):  
Jun Ni ◽  
Lili Yao ◽  
Jingchao Zhang ◽  
Weixing Cao ◽  
Yan Zhu ◽  
...  
Keyword(s):  
Monitoring System ◽  
Unmanned Aerial Vehicle ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Aerial Vehicle

scholarly journals Computer vision-based techniques and path planning strategy in a slope monitoring system using unmanned aerial vehicle

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142090430 ◽  
Author(s):  
Qing Li ◽  
Gaochen Min ◽  
Peng Chen ◽  
Yukun Liu ◽  
Siyu Tian ◽  
...  
Keyword(s):  
Monitoring System ◽  
Unmanned Aerial Vehicle ◽  
Semantic Segmentation ◽  
Histogram Equalization ◽  
Visual Features ◽  
Planning Strategy ◽  
Unstructured Environments ◽  
Aerial Vehicle ◽  
Slope Monitoring ◽  
Better Than

Unmanned aerial vehicle is a typical field robot which can work in many unstructured environments like mines, forests, and even radiation areas. In our mine monitoring system built in a northeast province of China, special designed unmanned aerial vehicle is applied to take photos and perceive the environment. We select a series of image-based techniques to process aerial pictures to monitor the slope. The visual features are initially refined by histogram equalization. Then, the rocks and cracks can be detected by different digital image processing operators, like Canny, so as to assess displacements. Advanced semantic segmentation model, U-Net, is also selected to process the problem. Experimental results show that both Canny and U-Net can perceive the edges in pictures effectively, better than other operators. In addition, we model the inspection mission for mine slopes into a traveling salesman problem, then plan the path for unmanned aerial vehicle by swarm intelligence-based optimization.

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