GAT-Enhanced YOLOv8_L with Dilated Encoder for Multi-Scale Space Object Detection Article Swipe
Haifeng Zhang
,
Han Ai
,
Donglin Xue
,
Zeyu He
,
Haoran Zhu
,
Delian Liu
,
Jianzhong Cao
,
Chao Mei
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17132119
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17132119
The problem of inadequate object detection accuracy in complex remote sensing scenarios has been identified as a primary concern. Traditional YOLO-series algorithms encounter challenges such as poor robustness in small object detection and significant interference from complex backgrounds. In this paper, a multi-scale feature fusion framework based on an improved version of YOLOv8_L is proposed. The combination of a graph attention network (GAT) and Dilated Encoder network significantly improves the algorithm detection and recognition performance for space remote sensing objects. It mainly includes abandoning the original Feature Pyramid Network (FPN) structure, proposing an adaptive fusion strategy based on multi-level features of backbone network, enhancing the expression ability of multi-scale objects through upsampling and feature stacking, and reconstructing the FPN. The local features extracted by convolutional neural networks are mapped to graph-structured data, and the nodal attention mechanism of GAT is used to capture the global topological association of space objects, which makes up for the deficiency of the convolutional operation in weight allocation and realizes GAT integration. The Dilated Encoder network is introduced to cover different-scale targets by differentiating receptive fields, and the feature weight allocation is optimized by combining it with a Convolutional Block Attention Module (CBAM). According to the characteristics of space missions, an annotated dataset containing 8000 satellite and space station images is constructed, covering a variety of lighting, attitude and scale scenes, and providing benchmark support for model training and verification. Experimental results on the space object dataset reveal that the enhanced algorithm achieves a mean average precision (mAP) of 97.2%, representing a 2.1% improvement over the original YOLOv8_L. Comparative experiments with six other models demonstrate that the proposed algorithm outperforms its counterparts. Ablation studies further validate the synergistic effect between the graph attention network (GAT) and the Dilated Encoder. The results indicate that the model maintains a high detection accuracy under challenging conditions, including strong light interference, multi-scale variations, and low-light environments.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs17132119
- https://www.mdpi.com/2072-4292/17/13/2119/pdf?version=1750425643
- OA Status
- gold
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411471376
All OpenAlex metadata
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- OpenAlex ID
-
https://openalex.org/W4411471376Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs17132119Digital Object Identifier
- Title
-
GAT-Enhanced YOLOv8_L with Dilated Encoder for Multi-Scale Space Object DetectionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-20Full publication date if available
- Authors
-
Haifeng Zhang, Han Ai, Donglin Xue, Zeyu He, Haoran Zhu, Delian Liu, Jianzhong Cao, Chao MeiList of authors in order
- Landing page
-
https://doi.org/10.3390/rs17132119Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/17/13/2119/pdf?version=1750425643Direct 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://www.mdpi.com/2072-4292/17/13/2119/pdf?version=1750425643Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Encoder, Pattern recognition (psychology), Robustness (evolution), Graph, Feature (linguistics), Feature vector, Image stitching, Object detection, Computer vision, Theoretical computer science, Operating system, Linguistics, Gene, Chemistry, Biochemistry, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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45Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.lighting, | 221 |
| abstract_inverted_index.low-light | 315 |
| abstract_inverted_index.maintains | 300 |
| abstract_inverted_index.mechanism | 136 |
| abstract_inverted_index.missions, | 204 |
| abstract_inverted_index.operation | 159 |
| abstract_inverted_index.optimized | 187 |
| abstract_inverted_index.precision | 251 |
| abstract_inverted_index.proposed. | 54 |
| abstract_inverted_index.proposing | 91 |
| abstract_inverted_index.providing | 227 |
| abstract_inverted_index.receptive | 179 |
| abstract_inverted_index.satellite | 210 |
| abstract_inverted_index.scenarios | 11 |
| abstract_inverted_index.stacking, | 114 |
| abstract_inverted_index.abandoning | 83 |
| abstract_inverted_index.algorithms | 21 |
| abstract_inverted_index.allocation | 162, 185 |
| abstract_inverted_index.challenges | 23 |
| abstract_inverted_index.containing | 208 |
| abstract_inverted_index.deficiency | 155 |
| abstract_inverted_index.expression | 105 |
| abstract_inverted_index.identified | 14 |
| abstract_inverted_index.inadequate | 3 |
| abstract_inverted_index.introduced | 172 |
| abstract_inverted_index.robustness | 27 |
| abstract_inverted_index.structure, | 90 |
| abstract_inverted_index.upsampling | 111 |
| abstract_inverted_index.Comparative | 263 |
| abstract_inverted_index.Traditional | 19 |
| abstract_inverted_index.YOLO-series | 20 |
| abstract_inverted_index.association | 146 |
| abstract_inverted_index.challenging | 306 |
| abstract_inverted_index.combination | 56 |
| abstract_inverted_index.conditions, | 307 |
| abstract_inverted_index.demonstrate | 269 |
| abstract_inverted_index.experiments | 264 |
| abstract_inverted_index.improvement | 258 |
| abstract_inverted_index.multi-level | 98 |
| abstract_inverted_index.multi-scale | 42, 108, 312 |
| abstract_inverted_index.outperforms | 274 |
| abstract_inverted_index.performance | 74 |
| abstract_inverted_index.recognition | 73 |
| abstract_inverted_index.significant | 33 |
| abstract_inverted_index.synergistic | 282 |
| abstract_inverted_index.topological | 145 |
| abstract_inverted_index.variations, | 313 |
| abstract_inverted_index.Experimental | 235 |
| abstract_inverted_index.backgrounds. | 37 |
| abstract_inverted_index.constructed, | 216 |
| abstract_inverted_index.integration. | 166 |
| abstract_inverted_index.interference | 34 |
| abstract_inverted_index.representing | 255 |
| abstract_inverted_index.Convolutional | 193 |
| abstract_inverted_index.convolutional | 124, 158 |
| abstract_inverted_index.counterparts. | 276 |
| abstract_inverted_index.environments. | 316 |
| abstract_inverted_index.interference, | 311 |
| abstract_inverted_index.significantly | 67 |
| abstract_inverted_index.verification. | 234 |
| abstract_inverted_index.reconstructing | 116 |
| abstract_inverted_index.characteristics | 201 |
| abstract_inverted_index.different-scale | 175 |
| abstract_inverted_index.differentiating | 178 |
| abstract_inverted_index.graph-structured | 130 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5100399667 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I149594827, https://openalex.org/I194716290, https://openalex.org/I19820366, https://openalex.org/I4210144662 |
| citation_normalized_percentile.value | 0.22968245 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |