Paper Info
Title
Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery.
Abstract
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.
Year
DOI
Venue
2019
10.3390/s19071651
SENSORS
Keywords
Field
DocType
deep learning,convolutional neural networks,unmanned aerial vehicle,bird detection
Convolutional neural network,Remote sensing,Electronic engineering,Artificial intelligence,Engineering,Deep learning,Aerial imagery
Journal
Volume
Issue
ISSN
19
7.0
1424-8220
Citations 
PageRank 
References 
1
0.36
0
Authors
5
Name
Order
Citations
PageRank
Suk-Ju Hong110.36
Yunhyeok Han210.36
Sang-Yeon Kim310.36
Ah-Yeong Lee421.18
Ghiseok Kim562.16