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A UAV Detection Algorithm Based on an Artificial Neural Network
Open accessAbstract
This work was supported in part by the National Natural Science Foundation of China under Grant 61701462 and Grant 41527901, in part by the Qingdao National Laboratory for Marine Science and Technology under Grant 2017ASKJ01, in part by the Qingdao Science and Technology Plan under Grant 17-1-1-7-jch, in part by the Fundamental Research Funds for the Central Universities under Grant 201713018, in part by the Shandong Province Natural Science Foundation under Grant ZR2017BF023, in part by the China Postdoctoral Science Foundation funded project under Grant 2017M612223, and in part by the Shandong Province Postdoctoral Innovation Project under Grant 201703032.
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“The algorithms for detecting UAVs particularly employ AI, for example, Zhang et al. [92] presented a UAV detection algorithm based on a artificial neural network (ANN).”
From A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022) · cites this paper as [92]
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Cited by
A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges
McEnroe, P., Wang, S., & Liyanage, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3176400 · Cited by 482
Metadata
- Authors
- Zhang, H., Cao, C., Xu, L., & Gulliver, T. A.
- Affiliations
- Ocean University of China (CN)
- Qingdao University of Science and Technology (CN)
- University of Victoria (CA)
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 24720-24728
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2801173680
- Cited by
- 93
- References
- 0
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 16 · 2023: 6 · 2022: 14 · 2021: 19 · 2020: 13 · 2019: 10 · 2018: 3
- FWCI
- 5.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61701462, 41527901)
- Qingdao National Laboratory for Marine Science and Technology (2017ASKJ01)
- Qingdao Science and Technology Plan (17-1-1-7-jch)
- Fundamental Research Funds for the Central Universities (201713018)
- Shandong Province Natural Science Foundation (ZR2017BF023)
- China Postdoctoral Science Foundation (2017M612223)
- Shandong Province Postdoctoral Innovation Project (201703032)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Aerospace Engineering › Advanced SAR Imaging Techniques
- Keywords
- unmanned aerial vehicle detection, artificial neural networks, object detection
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- CoChat. (2026). Citation check: A UAV Detection Algorithm Based on an Artificial Neural Network. https://app.cochat.ai/papers/10.1109/access.2018.2831911
- The paper
- Zhang, H., Cao, C., Xu, L., & Gulliver, T. A. (2018). A UAV Detection Algorithm Based on an Artificial Neural Network. IEEE Access. https://doi.org/10.1109/access.2018.2831911
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.