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Cited by 227Advanced Neural Network ApplicationsSmart Agriculture and AIDomain Adaptation and Few-Shot Learning

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

Open access
Hong, Suk-Ju, Han, Yunhyeok, Kim, Sang-Yeon, Lee, Ah-Yeong, Kim, Ghiseok·2019·Sensors
DOI 10.3390/s19071651PMC PMC6479331
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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.

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  • YOLOv3 achieved 91.80% average precision for bird detection on UAV aerial images.

    “| YOLOv3 [98] | IoU | YOLOv3 achieved 91.80% average precision for bird detection. | UAV aerial images |”

    From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [98]

    Section 3.1 (page unavailable) reports that YOLO models achieved approximately 90% average precision for bird detection at an IoU threshold of 0.3. Section 2.3 mentions YOLOv3, but the available results do not assign the precise 91.80% figure to that version; the exact value is therefore unsupported by our copy. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Hong, S.-J., Han, Y., Kim, S.-Y., Lee, A.-Y., & Kim, G.
Affiliations
Seoul National University (KR)
Corresponding author
Kim, Ghiseok
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2019-04-06
Volume
19
Issue
7
Pages
1651
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s19071651
PMC
PMC6479331
OpenAlex
W2929565236
Cited by
227
References
5
Citations by year
2026: 20 · 2025: 30 · 2024: 46 · 2023: 42 · 2022: 30 · 2021: 38 · 2020: 15 · 2019: 6
FWCI
9.94
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (317026-02)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
bird detection, UAV imagery, object detection, Faster R-CNN, YOLO, R-FCN, SSD, RetinaNet, migratory birds, avian influenza surveillance, bird population monitoring, bird habitat
MeSH terms
Machine Learning, Deep Learning, Animals, Birds, Neural Networks, Computer, Remote Sensing Technology

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