scholarly journals Rain Gutter Detection in Aerial Images for Aedes aegypti Mosquito Prevention

2020 ◽  
Author(s):  
Lucas Rossi ◽  
André Backes ◽  
Jefferson Souza
Keyword(s):  
Image Processing ◽  
Aedes Aegypti ◽  
Unmanned Aerial Vehicle ◽  
Digital Image Processing ◽  
Yellow Fever ◽  
Automatic System ◽  
Aerial Images ◽  
Aerial Vehicle ◽  
Mosquito Breeding ◽  
Breeding Location

The detection of Aedes aegypti mosquito is essential in the prevention process of serious diseases such as dengue, yellow fever, chikungunya, and Zika virus. Common approaches consist of surveillance agents who need to enter residences to find and eliminate these outbreaks, but often they are unable to do this work due to the absence or resistance of the resident. This paper proposes an automatic system that uses aerial images obtained through a camera coupled from an Unmanned Aerial Vehicle (UAV) to identify rain gutters from a shed that may be mosquitoes’ foci. We use Digital Image Processing (DIP) techniques to differentiate the objects that may or may not be those foci of the mosquito-breeding. The experimental results show that the system is capable of automatically detecting the appropriately mosquito-breeding location.

Implementation of vision-based automatic guidance system on a fixed-wing unmanned aerial vehicle

2012 ◽  
Vol 116 (1183) ◽  
pp. 895-914 ◽  
Author(s):  
C-S Lee ◽  
F-B Hsiao
Keyword(s):  
Image Processing ◽  
Unmanned Aerial Vehicle ◽  
Low Cost ◽  
Image Plane ◽  
Guidance System ◽  
Aerial Images ◽  
Colour Properties ◽  
Image Processing Algorithm ◽  
Automatic Guidance ◽  
Aerial Vehicle

Abstract This paper presents the design and implementation of a vision-based automatic guidance system on a fixed-wing unmanned aerial vehicle (UAV). The system utilises a low-cost ordinary video camera and simple but efficient image processing techniques widely used in computer-vision technology. The paper focuses on the identification and extraction of geographical tracks such as rivers, coastlines, and roads from real-time aerial images. The image processing algorithm primarily uses colour properties to isolate the geographical track of interest from its background. Hough transform is eventually used to curve-fit the profile of the track which yields a reference line on the image plane. A guidance algorithm is then derived based on this information. In order to test the vision-based automatic guidance system in the laboratory without actually flying the UAV, a hardware-in-the-loop simulation system is developed. Description regarding the system and significant simulation result are presented in the paper. Finally, an actual test flight where the UAV successfully follows a stretch of a river under automatic vision-based guidance is also presented and discussed.

scholarly journals Utilizing drone technology in the civil engineering

2019 ◽  
Vol 14 (1) ◽  
pp. 27-37
Author(s):  
Matúš Tkáč ◽  
Peter Mésároš
Keyword(s):  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Civil Engineering ◽  
Aerial Images ◽  
General Overview ◽  
Aerial Photogrammetry ◽  
Different Types ◽  
Aerial Vehicle

Abstract An unmanned aerial vehicle (UAVs), also known as drone technology, is used for different types of application in the civil engineering. Drones as a tools that increase communication between construction participants, improves site safety, uses topographic measurements of large areas, with using principles of aerial photogrammetry is possible to create buildings aerial surveying, bridges, roads, highways, saves project time and costs, etc. The use of UAVs in the civil engineering can brings many benefits; creating real-time aerial images from the building objects, overviews reveal assets and challenges, as well as the broad lay of the land, operators can share the imaging with personnel on site, in headquarters and with sub-contractors, planners can meet virtually to discuss project timing, equipment needs and challenges presented by the terrain. The aim of this contribution is to create a general overview of the use of UAVs in the civil engineering. The contribution also contains types of UAVs used for construction purposes, their advantages and also disadvantages.

Fast Data Acquisition of Aerial Images Using Unmanned Aerial Vehicle System

2014 ◽  
Vol 3 (3) ◽  
pp. 162
Author(s):  
Norhadija Darwin ◽  
Anuar Ahmad
Keyword(s):  
Data Acquisition ◽  
Root Mean Square ◽  
Unmanned Aerial Vehicle ◽  
Large Scale ◽  
Digital Camera ◽  
Aerial Images ◽  
Mean Square ◽  
Fast Data Acquisition ◽  
Vehicle System ◽  
Aerial Vehicle

The present work discusses the technique and methodology of analysing the potential of fast data acquisition of aerial images using unmanned aerial vehicle system. This study utilizes UAV system for large scale mapping by using digital camera attached to the UAV. UAV is developed from the low-altitude photogrammetric mapping to perform the accuracy of the aerial photography and the resolution of the image. The Ground Control Points (GCPs) and Check Points (CPs) are established using Rapid Static techniques through GPS observation for registration purpose in photogrammetric process. The GCPs is used in the photogrammetric processes to produce photogrammetric output while the CP is employed for accuracy assessment. A Pentax Optio W90 consumer digital camera is also used in image acquisition of the aerial photograph. Besides, this study also involves image processing and map production using Erdas Imagine 8.6 software. The accuracy of the orthophoto is determined using the equation of Root Mean Square Error (RMSE). The final result from orthophoto is compared to the ground survey using total station to show the different accuracy of DEM and planimetric survey. It is discovered that root mean square errors obtained from UAV system are ± 0.510, ± 0.564 and ± 0.622 for coordinate x, y and z respectively. Hence, it can be concluded that the accuracy obtained from UAV system is achieved in sub meter. In a nutshell, UAV system has potential use for large scale mapping in field of surveying and other diversified environmental applications especially for small area which has limited time and less man power.

scholarly journals Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1651 ◽  
Author(s):  
Suk-Ju Hong ◽  
Yunhyeok Han ◽  
Sang-Yeon Kim ◽  
Ah-Yeong Lee ◽  
Ghiseok Kim
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Migratory Bird ◽  
Aerial Images ◽  
Aerial Photographs ◽  
Detection Methods ◽  
Single Shot ◽  
Convolutional Network ◽  
Aerial Vehicle ◽  
Bird Detection

Wild birds are monitored with the important objectives of identifying their habitats and estimating the size of their populations. Especially in the case of migratory bird, they are significantly recorded during specific periods of time to forecast any possible spread of animal disease such as avian influenza. This study led to the construction of deep-learning-based object-detection models with the aid of aerial photographs collected by an unmanned aerial vehicle (UAV). The dataset containing the aerial photographs includes diverse images of birds in various bird habitats and in the vicinity of lakes and on farmland. In addition, aerial images of bird decoys are captured to achieve various bird patterns and more accurate bird information. Bird detection models such as Faster Region-based Convolutional Neural Network (R-CNN), Region-based Fully Convolutional Network (R-FCN), Single Shot MultiBox Detector (SSD), Retinanet, and You Only Look Once (YOLO) were created and the performance of all models was estimated by comparing their computing speed and average precision. The test results show Faster R-CNN to be the most accurate and YOLO to be the fastest among the models. The combined results demonstrate that the use of deep-learning-based detection methods in combination with UAV aerial imagery is fairly suitable for bird detection in various environments.

scholarly journals Mapping Reflectance Anisotropy of a Potato Canopy Using Aerial Images Acquired with an Unmanned Aerial Vehicle

2017 ◽  
Vol 9 (5) ◽  
pp. 417 ◽  
Author(s):  
Peter Roosjen ◽  
Juha Suomalainen ◽  
Harm Bartholomeus ◽  
Lammert Kooistra ◽  
Jan Clevers
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Aerial Images ◽  
Reflectance Anisotropy ◽  
Aerial Vehicle
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