A Class-Incremental Detection Method of Remote Sensing Images Based on Selective Distillation Article Swipe
Hang Ruan
,
Jian Peng
,
Ye Chen
,
Silu He
,
Zhenshi Zhang
,
Haifeng Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/sym14102100
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/sym14102100
With the rapid development of remote sensing technology and the growing demand for applications, the classical deep learning-based object detection model is bottlenecked in processing incremental data, especially in the increasing classes of detected objects. It requires models to sequentially learn new classes of objects based on the current model, while preserving old categories-related knowledge. Existing class-incremental detection methods achieve this goal mainly by constraining the optimization trajectory in the feature of output space. However, these works neglect the case where the previously learned background is a new category to learn, resulting in performance degradation in the new category because of the conflict between remaining the background-related knowledge or updating the background-related knowledge. This paper proposes a novel class-incremental detection method incorporated with the teacher-student architecture and the selective distillation (SDCID) strategy. Specifically, it is the asymmetry architecture, i.e., the teacher network temporarily stores historical knowledge of previously learned objects, and the student network integrates historical knowledge from the teacher network with the newly learned object-related knowledge, respectively. This asymmetry architecture reveals the significance of the distinct representation of history knowledge and new knowledge in incremental detection. Furthermore, SDCID selectively masks the shared subobject of new images to learn and previously learned background, while learning new categories of images and then transfers the classification results of the student model to the background class following the judgment model of the teacher model. This manner avoids interferences between the background category-related knowledge from a teacher model and the learning of other new classes of objects. In addition, we proposed a new incremental learning evaluation metric, C-SP, to comprehensively evaluate the incremental learning stability and plasticity performance. We verified the proposed method on two object detection datasets of remote sensing images, i.e., DIOR and DOTA. The experience results in accuracy and C-SP suggest that the proposed method surpasses existing class-incremental detection methods. We further analyzed the influence of the mask component in our method and the hyper-parameters sensitive to our method.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/sym14102100
- https://www.mdpi.com/2073-8994/14/10/2100/pdf?version=1666679193
- OA Status
- gold
- Cited By
- 5
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4304184394
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4304184394Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/sym14102100Digital Object Identifier
- Title
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A Class-Incremental Detection Method of Remote Sensing Images Based on Selective DistillationWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-09Full publication date if available
- Authors
-
Hang Ruan, Jian Peng, Ye Chen, Silu He, Zhenshi Zhang, Haifeng LiList of authors in order
- Landing page
-
https://doi.org/10.3390/sym14102100Publisher landing page
- PDF URL
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https://www.mdpi.com/2073-8994/14/10/2100/pdf?version=1666679193Direct 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/2073-8994/14/10/2100/pdf?version=1666679193Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Object detection, Class (philosophy), Machine learning, Object (grammar), Feature (linguistics), Pattern recognition (psychology), Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 3, 2023: 1Per-year citation counts (last 5 years)
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34Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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