Object Detection Based on Adaptive Feature-Aware Method in Optical Remote Sensing Images Article Swipe
Jiaqi Wang
,
Zhihui Gong
,
Xiangyun Liu
,
Haitao Guo
,
Donghang Yu
,
Lei Ding
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14153616
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14153616
Object detection is used widely in remote sensing image interpretation. Although most models used for object detection have achieved high detection accuracy, computational complexity and low detection speeds limit their application in real-time detection tasks. This study developed an adaptive feature-aware method of object detection in remote sensing images based on the single-shot detector architecture called adaptive feature-aware detector (AFADet). Self-attention is used to extract high-level semantic information derived from deep feature maps for spatial localization of objects and the model is improved in localizing objects. The adaptive feature-aware module is used to perform adaptive cross-scale depth fusion of different-scale feature maps to improve the learning ability of the model and reduce the influence of complex backgrounds in remote sensing images. The focal loss is used during training to address the positive and negative sample imbalance problem, reduce the influence of the loss value dominated by easily classified samples, and enhance the stability of model training. Experiments are conducted on three object detection datasets, and the results are compared with those of the classical and recent object detection algorithms. The mean average precision(mAP) values are 66.12%, 95.54%, and 86.44% for three datasets, which suggests that AFADet can detect remote sensing images in real-time with high accuracy and can effectively balance detection accuracy and speed.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14153616
- https://www.mdpi.com/2072-4292/14/15/3616/pdf?version=1659498843
- OA Status
- gold
- Cited By
- 14
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4288718220
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4288718220Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs14153616Digital Object Identifier
- Title
-
Object Detection Based on Adaptive Feature-Aware Method in Optical Remote Sensing ImagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-28Full publication date if available
- Authors
-
Jiaqi Wang, Zhihui Gong, Xiangyun Liu, Haitao Guo, Donghang Yu, Lei DingList of authors in order
- Landing page
-
https://doi.org/10.3390/rs14153616Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/14/15/3616/pdf?version=1659498843Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-4292/14/15/3616/pdf?version=1659498843Direct OA link when available
- Concepts
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Computer science, Object detection, Artificial intelligence, Feature (linguistics), Computer vision, Detector, Pattern recognition (psychology), Remote sensing, Geology, Linguistics, Philosophy, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 5, 2023: 6Per-year citation counts (last 5 years)
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59Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Remote Sensing |
| best_oa_location.landing_page_url | https://doi.org/10.3390/rs14153616 |
| primary_location.id | doi:10.3390/rs14153616 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S43295729 |
| primary_location.source.issn | 2072-4292 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2072-4292 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Remote Sensing |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2072-4292/14/15/3616/pdf?version=1659498843 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Remote Sensing |
| primary_location.landing_page_url | https://doi.org/10.3390/rs14153616 |
| publication_date | 2022-07-28 |
| publication_year | 2022 |
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| referenced_works_count | 59 |
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| abstract_inverted_index.by | 145 |
| abstract_inverted_index.in | 5, 31, 45, 83, 117, 201 |
| abstract_inverted_index.is | 2, 61, 81, 90, 124 |
| abstract_inverted_index.of | 42, 76, 98, 107, 114, 140, 153, 171 |
| abstract_inverted_index.on | 50, 159 |
| abstract_inverted_index.to | 63, 92, 102, 128 |
| abstract_inverted_index.The | 86, 121, 179 |
| abstract_inverted_index.and | 24, 78, 110, 132, 149, 164, 174, 187, 206, 212 |
| abstract_inverted_index.are | 157, 167, 184 |
| abstract_inverted_index.can | 196, 207 |
| abstract_inverted_index.for | 14, 73, 189 |
| abstract_inverted_index.low | 25 |
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| abstract_inverted_index.This | 35 |
| abstract_inverted_index.deep | 70 |
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| abstract_inverted_index.loss | 123, 142 |
| abstract_inverted_index.maps | 72, 101 |
| abstract_inverted_index.mean | 180 |
| abstract_inverted_index.most | 11 |
| abstract_inverted_index.that | 194 |
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| abstract_inverted_index.with | 169, 203 |
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| abstract_inverted_index.focal | 122 |
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| abstract_inverted_index.study | 36 |
| abstract_inverted_index.their | 29 |
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| abstract_inverted_index.value | 143 |
| abstract_inverted_index.which | 192 |
| abstract_inverted_index.86.44% | 188 |
| abstract_inverted_index.AFADet | 195 |
| abstract_inverted_index.Object | 0 |
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| abstract_inverted_index.detect | 197 |
| abstract_inverted_index.during | 126 |
| abstract_inverted_index.easily | 146 |
| abstract_inverted_index.fusion | 97 |
| abstract_inverted_index.images | 48, 200 |
| abstract_inverted_index.method | 41 |
| abstract_inverted_index.models | 12 |
| abstract_inverted_index.module | 89 |
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| abstract_inverted_index.reduce | 111, 137 |
| abstract_inverted_index.remote | 6, 46, 118, 198 |
| abstract_inverted_index.sample | 134 |
| abstract_inverted_index.speed. | 213 |
| abstract_inverted_index.speeds | 27 |
| abstract_inverted_index.tasks. | 34 |
| abstract_inverted_index.values | 183 |
| abstract_inverted_index.widely | 4 |
| abstract_inverted_index.66.12%, | 185 |
| abstract_inverted_index.95.54%, | 186 |
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| abstract_inverted_index.address | 129 |
| abstract_inverted_index.average | 181 |
| abstract_inverted_index.balance | 209 |
| abstract_inverted_index.complex | 115 |
| abstract_inverted_index.derived | 68 |
| abstract_inverted_index.enhance | 150 |
| abstract_inverted_index.extract | 64 |
| abstract_inverted_index.feature | 71, 100 |
| abstract_inverted_index.images. | 120 |
| abstract_inverted_index.improve | 103 |
| abstract_inverted_index.objects | 77 |
| abstract_inverted_index.perform | 93 |
| abstract_inverted_index.results | 166 |
| abstract_inverted_index.sensing | 7, 47, 119, 199 |
| abstract_inverted_index.spatial | 74 |
| abstract_inverted_index.Although | 10 |
| abstract_inverted_index.accuracy | 205, 211 |
| abstract_inverted_index.achieved | 18 |
| abstract_inverted_index.adaptive | 39, 56, 87, 94 |
| abstract_inverted_index.compared | 168 |
| abstract_inverted_index.detector | 53, 58 |
| abstract_inverted_index.improved | 82 |
| abstract_inverted_index.learning | 105 |
| abstract_inverted_index.negative | 133 |
| abstract_inverted_index.objects. | 85 |
| abstract_inverted_index.positive | 131 |
| abstract_inverted_index.problem, | 136 |
| abstract_inverted_index.samples, | 148 |
| abstract_inverted_index.semantic | 66 |
| abstract_inverted_index.suggests | 193 |
| abstract_inverted_index.training | 127 |
| abstract_inverted_index.(AFADet). | 59 |
| abstract_inverted_index.accuracy, | 21 |
| abstract_inverted_index.classical | 173 |
| abstract_inverted_index.conducted | 158 |
| abstract_inverted_index.datasets, | 163, 191 |
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| abstract_inverted_index.developed | 37 |
| abstract_inverted_index.dominated | 144 |
| abstract_inverted_index.imbalance | 135 |
| abstract_inverted_index.influence | 113, 139 |
| abstract_inverted_index.real-time | 32, 202 |
| abstract_inverted_index.stability | 152 |
| abstract_inverted_index.training. | 155 |
| abstract_inverted_index.classified | 147 |
| abstract_inverted_index.complexity | 23 |
| abstract_inverted_index.high-level | 65 |
| abstract_inverted_index.localizing | 84 |
| abstract_inverted_index.Experiments | 156 |
| abstract_inverted_index.algorithms. | 178 |
| abstract_inverted_index.application | 30 |
| abstract_inverted_index.backgrounds | 116 |
| abstract_inverted_index.cross-scale | 95 |
| abstract_inverted_index.effectively | 208 |
| abstract_inverted_index.information | 67 |
| abstract_inverted_index.single-shot | 52 |
| abstract_inverted_index.architecture | 54 |
| abstract_inverted_index.localization | 75 |
| abstract_inverted_index.computational | 22 |
| abstract_inverted_index.feature-aware | 40, 57, 88 |
| abstract_inverted_index.Self-attention | 60 |
| abstract_inverted_index.precision(mAP) | 182 |
| abstract_inverted_index.different-scale | 99 |
| abstract_inverted_index.interpretation. | 9 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5101656781 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I169689159 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.699999988079071 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.85373214 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |