Aerial Data Exploration: An in-Depth Study From Horizontal to Oriented Viewpoint Article Swipe
Nguyen D. Vo
,
Huong-Giang Ngo
,
Toan Le
,
Quang H. Nguyen
,
Danh-Du Doan
,
Binh P. Nguyen
,
Do Le-Huu
,
Nguyen Le-Duy
,
Huy Tran
,
Khanh-Duy Nguyen
,
Khang Nguyen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3371514
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3371514
The development of technological devices, such as satellites and drones, has made it easier to collect images and videos from the air. From these vast data sources, the problem of detecting objects in aerial images is formed to serve situations: rescue missions, social scenarios, security needs, and more. This issue attracts a lot of attention from researchers in computer vision because of its high applicability in real life. However, this problem faces many significant challenges due to the complex factors of the environment. To overcome these difficulties, many studies focus on developing effective models for detecting and tracking objects from UAV systems. In this paper, our primary emphasis is a comprehensive survey of methodologies about horizontal and oriented object detection. After researching many practical and innovative techniques in different approaches, we have carefully selected 15 methods for horizontal object detection and 18 for oriented object detection that were recently researched and developed to conduct classifications, surveys, and assessments. We also provide a taxonomy mind map to assist readers in gaining an overview of the methods we surveyed. Additionally, we examine 17 aerial image datasets collected from platforms and in various locations worldwide. These are typical datasets widely used to serve two primary tasks: horizontal object detection and oriented object detection. Then, we compared the experimental results of the previously introduced methods on four typical datasets: VisDrone, UAVDT, DOTAv1.0, and HRSC2016. The above datasets present diversity in the number of classes and the complexity of real-life conditions. Furthermore, they have been tested with various methods and shown remarkable results. For the reasons mentioned, we believe that updating and synthesizing the latest research will provide complete and comprehensive information, helping researchers to have more survey material for the development of the field of object detection in the future.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3371514
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10453557.pdf
- OA Status
- gold
- Cited By
- 4
- References
- 111
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392309300
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392309300Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3371514Digital Object Identifier
- Title
-
Aerial Data Exploration: An in-Depth Study From Horizontal to Oriented ViewpointWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Nguyen D. Vo, Huong-Giang Ngo, Toan Le, Quang H. Nguyen, Danh-Du Doan, Binh P. Nguyen, Do Le-Huu, Nguyen Le-Duy, Huy Tran, Khanh-Duy Nguyen, Khang NguyenList of authors in order
- Landing page
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https://doi.org/10.1109/access.2024.3371514Publisher landing page
- PDF URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10453557.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10453557.pdfDirect OA link when available
- Concepts
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Computer science, Object detection, Drone, Bounding overwatch, Artificial intelligence, Object (grammar), Computer vision, Focus (optics), Data science, Human–computer interaction, Pattern recognition (psychology), Genetics, Physics, Biology, OpticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2025: 1, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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111Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.UAVDT, | 226 |
| abstract_inverted_index.aerial | 33, 181 |
| abstract_inverted_index.assist | 166 |
| abstract_inverted_index.easier | 13 |
| abstract_inverted_index.formed | 36 |
| abstract_inverted_index.images | 16, 34 |
| abstract_inverted_index.latest | 269 |
| abstract_inverted_index.models | 93 |
| abstract_inverted_index.needs, | 45 |
| abstract_inverted_index.number | 237 |
| abstract_inverted_index.object | 118, 138, 144, 204, 208, 291 |
| abstract_inverted_index.paper, | 104 |
| abstract_inverted_index.rescue | 40 |
| abstract_inverted_index.social | 42 |
| abstract_inverted_index.survey | 111, 282 |
| abstract_inverted_index.tasks: | 202 |
| abstract_inverted_index.tested | 250 |
| abstract_inverted_index.videos | 18 |
| abstract_inverted_index.vision | 59 |
| abstract_inverted_index.widely | 196 |
| abstract_inverted_index.because | 60 |
| abstract_inverted_index.believe | 263 |
| abstract_inverted_index.classes | 239 |
| abstract_inverted_index.collect | 15 |
| abstract_inverted_index.complex | 78 |
| abstract_inverted_index.conduct | 153 |
| abstract_inverted_index.drones, | 9 |
| abstract_inverted_index.examine | 179 |
| abstract_inverted_index.factors | 79 |
| abstract_inverted_index.future. | 295 |
| abstract_inverted_index.gaining | 169 |
| abstract_inverted_index.helping | 277 |
| abstract_inverted_index.methods | 135, 174, 220, 253 |
| abstract_inverted_index.objects | 31, 98 |
| abstract_inverted_index.present | 233 |
| abstract_inverted_index.primary | 106, 201 |
| abstract_inverted_index.problem | 28, 70 |
| abstract_inverted_index.provide | 160, 272 |
| abstract_inverted_index.readers | 167 |
| abstract_inverted_index.reasons | 260 |
| abstract_inverted_index.results | 215 |
| abstract_inverted_index.studies | 88 |
| abstract_inverted_index.typical | 194, 223 |
| abstract_inverted_index.various | 189, 252 |
| abstract_inverted_index.However, | 68 |
| abstract_inverted_index.attracts | 50 |
| abstract_inverted_index.compared | 212 |
| abstract_inverted_index.complete | 273 |
| abstract_inverted_index.computer | 58 |
| abstract_inverted_index.datasets | 183, 195, 232 |
| abstract_inverted_index.devices, | 4 |
| abstract_inverted_index.emphasis | 107 |
| abstract_inverted_index.material | 283 |
| abstract_inverted_index.oriented | 117, 143, 207 |
| abstract_inverted_index.overcome | 84 |
| abstract_inverted_index.overview | 171 |
| abstract_inverted_index.recently | 148 |
| abstract_inverted_index.research | 270 |
| abstract_inverted_index.results. | 257 |
| abstract_inverted_index.security | 44 |
| abstract_inverted_index.selected | 133 |
| abstract_inverted_index.sources, | 26 |
| abstract_inverted_index.surveys, | 155 |
| abstract_inverted_index.systems. | 101 |
| abstract_inverted_index.taxonomy | 162 |
| abstract_inverted_index.tracking | 97 |
| abstract_inverted_index.updating | 265 |
| abstract_inverted_index.DOTAv1.0, | 227 |
| abstract_inverted_index.HRSC2016. | 229 |
| abstract_inverted_index.VisDrone, | 225 |
| abstract_inverted_index.attention | 54 |
| abstract_inverted_index.carefully | 132 |
| abstract_inverted_index.collected | 184 |
| abstract_inverted_index.datasets: | 224 |
| abstract_inverted_index.detecting | 30, 95 |
| abstract_inverted_index.detection | 139, 145, 205, 292 |
| abstract_inverted_index.developed | 151 |
| abstract_inverted_index.different | 128 |
| abstract_inverted_index.diversity | 234 |
| abstract_inverted_index.effective | 92 |
| abstract_inverted_index.locations | 190 |
| abstract_inverted_index.missions, | 41 |
| abstract_inverted_index.platforms | 186 |
| abstract_inverted_index.practical | 123 |
| abstract_inverted_index.real-life | 244 |
| abstract_inverted_index.surveyed. | 176 |
| abstract_inverted_index.challenges | 74 |
| abstract_inverted_index.complexity | 242 |
| abstract_inverted_index.detection. | 119, 209 |
| abstract_inverted_index.developing | 91 |
| abstract_inverted_index.horizontal | 115, 137, 203 |
| abstract_inverted_index.innovative | 125 |
| abstract_inverted_index.introduced | 219 |
| abstract_inverted_index.mentioned, | 261 |
| abstract_inverted_index.previously | 218 |
| abstract_inverted_index.remarkable | 256 |
| abstract_inverted_index.researched | 149 |
| abstract_inverted_index.satellites | 7 |
| abstract_inverted_index.scenarios, | 43 |
| abstract_inverted_index.techniques | 126 |
| abstract_inverted_index.worldwide. | 191 |
| abstract_inverted_index.approaches, | 129 |
| abstract_inverted_index.conditions. | 245 |
| abstract_inverted_index.development | 1, 286 |
| abstract_inverted_index.researchers | 56, 278 |
| abstract_inverted_index.researching | 121 |
| abstract_inverted_index.significant | 73 |
| abstract_inverted_index.situations: | 39 |
| abstract_inverted_index.Furthermore, | 246 |
| abstract_inverted_index.assessments. | 157 |
| abstract_inverted_index.environment. | 82 |
| abstract_inverted_index.experimental | 214 |
| abstract_inverted_index.information, | 276 |
| abstract_inverted_index.synthesizing | 267 |
| abstract_inverted_index.Additionally, | 177 |
| abstract_inverted_index.applicability | 64 |
| abstract_inverted_index.comprehensive | 110, 275 |
| abstract_inverted_index.difficulties, | 86 |
| abstract_inverted_index.methodologies | 113 |
| abstract_inverted_index.technological | 3 |
| abstract_inverted_index.classifications, | 154 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 11 |
| citation_normalized_percentile.value | 0.80695031 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |