DeepVision: Enhanced Drone Detection and Recognition in Visible Imagery through Deep Learning Networks Article Swipe
Hassan J. Al Dawasari
,
Muhammad Bilal
,
Muhammad Moinuddin
,
Kamran Arshad
,
Khaled Assaleh
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23218711
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23218711
Drones are increasingly capturing the world’s attention, transcending mere hobbies to revolutionize areas such as engineering, disaster aid, logistics, and airport protection, among myriad other fascinating applications. However, there is growing concern about the risks that they pose to physical infrastructure, particularly at airports, due to potential misuse. In recent times, numerous incidents involving unauthorized drones at airports disrupting flights have been reported. To solve this issue, this article introduces an innovative deep learning method proposed to effectively distinguish between drones and birds. Evaluating the suggested approach with a carefully assembled image dataset demonstrates exceptional performance, surpassing established detection systems previously proposed in the literature. Since drones can appear extremely small compared to other aerial objects, we developed a robust image-tiling technique with overlaps, which showed improved performance in the presence of very small drones. Moreover, drones are frequently mistaken for birds due to their resemblances in appearance and movement patterns. Among the various models tested, including SqueezeNet, MobileNetV2, ResNet18, and ResNet50, the SqueezeNet model exhibited superior performance for medium area ratios, achieving higher average precision (AP) of 0.770. In addition, SqueezeNet’s superior AP scores, faster detection times, and more stable precision-recall dynamics make it more suitable for real-time, accurate drone detection than the other existing CNN methods. The proposed approach has the ability to not only detect the presence or absence of drones in a particular area but also to accurately identify and differentiate between drones and birds. The dataset utilized in this research was obtained from a real-world dataset made available by a group of universities and research institutions as part of the 2020 Drone vs. Bird Detection Challenge. We have also tested the performance of the proposed model on an unseen dataset, further validating its better performance.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23218711
- https://www.mdpi.com/1424-8220/23/21/8711/pdf?version=1698304852
- OA Status
- gold
- Cited By
- 20
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388266596
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388266596Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s23218711Digital Object Identifier
- Title
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DeepVision: Enhanced Drone Detection and Recognition in Visible Imagery through Deep Learning NetworksWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-25Full publication date if available
- Authors
-
Hassan J. Al Dawasari, Muhammad Bilal, Muhammad Moinuddin, Kamran Arshad, Khaled AssalehList of authors in order
- Landing page
-
https://doi.org/10.3390/s23218711Publisher landing page
- PDF URL
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https://www.mdpi.com/1424-8220/23/21/8711/pdf?version=1698304852Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1424-8220/23/21/8711/pdf?version=1698304852Direct OA link when available
- Concepts
-
Drone, Artificial intelligence, Deep learning, Computer science, Aerial imagery, Computer vision, Recall, Machine learning, Computer security, Psychology, Genetics, Biology, Cognitive psychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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20Total citation count in OpenAlex
- Citations by year (recent)
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2025: 13, 2024: 7Per-year citation counts (last 5 years)
- References (count)
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31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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