EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV Article Swipe
Huiming Sun
,
Jiacheng Guo
,
Zibo Meng
,
Tianyun Zhang
,
Jianwu Fang
,
Yuewei Lin
,
Hongkai Yu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.05422
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.05422
Vehicle detection in Unmanned Aerial Vehicle (UAV) captured images has wide applications in aerial photography and remote sensing. There are many public benchmark datasets proposed for the vehicle detection and tracking in UAV images. Recent studies show that adding an adversarial patch on objects can fool the well-trained deep neural networks based object detectors, posing security concerns to the downstream tasks. However, the current public UAV datasets might ignore the diverse altitudes, vehicle attributes, fine-grained instance-level annotation in mostly side view with blurred vehicle roof, so none of them is good to study the adversarial patch based vehicle detection attack problem. In this paper, we propose a new dataset named EVD4UAV as an altitude-sensitive benchmark to evade vehicle detection in UAV with 6,284 images and 90,886 fine-grained annotated vehicles. The EVD4UAV dataset has diverse altitudes (50m, 70m, 90m), vehicle attributes (color, type), fine-grained annotation (horizontal and rotated bounding boxes, instance-level mask) in top view with clear vehicle roof. One white-box and two black-box patch based attack methods are implemented to attack three classic deep neural networks based object detectors on EVD4UAV. The experimental results show that these representative attack methods could not achieve the robust altitude-insensitive attack performance.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2403.05422
- https://arxiv.org/pdf/2403.05422
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392677769
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392677769Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2403.05422Digital Object Identifier
- Title
-
EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAVWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-08Full publication date if available
- Authors
-
Huiming Sun, Jiacheng Guo, Zibo Meng, Tianyun Zhang, Jianwu Fang, Yuewei Lin, Hongkai YuList of authors in order
- Landing page
-
https://arxiv.org/abs/2403.05422Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2403.05422Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2403.05422Direct OA link when available
- Concepts
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Benchmark (surveying), Altitude (triangle), Low altitude, Aeronautics, Computer science, Artificial intelligence, Remote sensing, Geography, Cartography, Engineering, Mathematics, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.sensing. | 17 |
| abstract_inverted_index.tracking | 30 |
| abstract_inverted_index.altitudes | 134 |
| abstract_inverted_index.annotated | 127 |
| abstract_inverted_index.benchmark | 22, 114 |
| abstract_inverted_index.black-box | 162 |
| abstract_inverted_index.detection | 1, 28, 98, 118 |
| abstract_inverted_index.detectors | 178 |
| abstract_inverted_index.vehicles. | 128 |
| abstract_inverted_index.white-box | 159 |
| abstract_inverted_index.altitudes, | 71 |
| abstract_inverted_index.annotation | 76, 143 |
| abstract_inverted_index.attributes | 139 |
| abstract_inverted_index.detectors, | 53 |
| abstract_inverted_index.downstream | 59 |
| abstract_inverted_index.(horizontal | 144 |
| abstract_inverted_index.adversarial | 40, 94 |
| abstract_inverted_index.attributes, | 73 |
| abstract_inverted_index.implemented | 168 |
| abstract_inverted_index.photography | 14 |
| abstract_inverted_index.applications | 11 |
| abstract_inverted_index.experimental | 182 |
| abstract_inverted_index.fine-grained | 74, 126, 142 |
| abstract_inverted_index.performance. | 197 |
| abstract_inverted_index.well-trained | 47 |
| abstract_inverted_index.instance-level | 75, 149 |
| abstract_inverted_index.representative | 187 |
| abstract_inverted_index.altitude-sensitive | 113 |
| abstract_inverted_index.altitude-insensitive | 195 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |