Multi-Feature Information Complementary Detector: A High-Precision Object Detection Model for Remote Sensing Images Article Swipe
Jiaqi Wang
,
Zhihui Gong
,
Xiangyun Liu
,
Haitao Guo
,
Jun Lu
,
Donghang Yu
,
Yuzhun Lin
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14184519
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14184519
Remote sensing for image object detection has numerous important applications. However, complex backgrounds and large object-scale differences pose considerable challenges in the detection task. To overcome these issues, we proposed a one-stage remote sensing image object detection model: a multi-feature information complementary detector (MFICDet). This detector contains a positive and negative feature guidance module (PNFG) and a global feature information complementary module (GFIC). Specifically, the PNFG is used to refine features that are beneficial for object detection and explore the noisy features in a complex background of abstract features. The proportion of beneficial features in the feature information stream is increased by suppressing noisy features. The GFIC uses pooling to compress the deep abstract features and improve the model’s ability to resist feature displacement and rotation. The pooling operation has the disadvantage of losing detailed feature information; thus, dilated convolution is introduced for feature complementation. Dilated convolution increases the receptive field of the model while maintaining an unchanged spatial resolution. This can improve the ability of the model to recognize long-distance dependent information and establish spatial location relationships between features. The detector proposed also improves the detection performance of objects at different scales in the same image using a dual multi-scale feature fusion strategy. Finally, classification and regression tasks are decoupled in space using a decoupled head. We experimented on the DIOR and NWPU VHR-10 datasets to demonstrate that the newly proposed MFICDet achieves competitive performance compared to current state-of-the-art detectors.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14184519
- https://www.mdpi.com/2072-4292/14/18/4519/pdf?version=1663079014
- OA Status
- gold
- Cited By
- 14
- References
- 81
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4295533017
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Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4295533017Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs14184519Digital Object Identifier
- Title
-
Multi-Feature Information Complementary Detector: A High-Precision Object Detection Model for Remote Sensing ImagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-09Full publication date if available
- Authors
-
Jiaqi Wang, Zhihui Gong, Xiangyun Liu, Haitao Guo, Jun Lu, Donghang Yu, Yuzhun LinList of authors in order
- Landing page
-
https://doi.org/10.3390/rs14184519Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/14/18/4519/pdf?version=1663079014Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2072-4292/14/18/4519/pdf?version=1663079014Direct OA link when available
- Concepts
-
Computer science, Artificial intelligence, Feature (linguistics), Computer vision, Object detection, Pooling, Detector, Pattern recognition (psychology), Convolution (computer science), Artificial neural network, Linguistics, Telecommunications, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2, 2023: 9, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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81Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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