A Dual-Branch Detector Based on the Multi-Granularity Dynamic Selection Mechanism for Remote Sensing Incremental Detection.
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| Title: | A Dual-Branch Detector Based on the Multi-Granularity Dynamic Selection Mechanism for Remote Sensing Incremental Detection. |
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| Authors: | Li, Shixi1 (AUTHOR), Wang, Weiji1 (AUTHOR), Xu, Yousheng1 (AUTHOR), Yao, Wei1 (AUTHOR) wyao@scuec.edu.cn, Xu, Shengzhou1 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p2032. 27p. |
| Subjects: | Remote sensing, Knowledge transfer, Object recognition (Computer vision), Machine learning |
| Abstract: | Highlights: What are the main findings? A dual-branch detector framework is proposed for remote sensing incremental object detection, which decouples the learning pathways of old and new classes to alleviate inter-class confusion and catastrophic forgetting. A multi-granularity dynamic selection (MDS) strategy and a sigmoid-based DIST loss are introduced to improve old-class knowledge transfer by selecting informative teacher responses and preserving inter-class and intra-class relationships. What are the implications of the main findings? The proposed framework provides a practical solution for updating remote sensing object detectors when new object categories emerge, without requiring full retraining with all historical data. Experiments on DIOR and DOTA demonstrate that the method can achieve a favorable balance between old-class retention and new-class adaptation, indicating its potential for continual remote sensing image interpretation in evolving scenarios. In practical remote sensing object detection tasks, the application of deep learning approaches often takes the form of incremental learning: when the application includes new target types that were not encountered during training, a pre-trained model must acquire new knowledge without suffering catastrophic forgetting. Among the various techniques proposed, knowledge distillation (KD)-based regularization has proven to be one of the most effective methods. Current KD-based approaches primarily focus on addressing inter-task confusion and optimizing feature selection during distillation processes. In this paper, we propose a dual-branch detector-independent learning framework and a multi-granularity dynamic selection strategy. The former decouples detection tasks for old and new classes to mitigate inter-class confusion, while the latter is a novel, exquisitely designed distillation mechanism that ensures precise transfer of critical old-class information. Moreover, we apply a DIST loss that aligns both inter-class and intra-class relations, further enhancing the fidelity of old-class knowledge transfer. Experiments on the DIOR and DOTA datasets demonstrate that our method significantly outperforms state-of-the-art incremental-learning approaches for remote-sensing object detection and exhibits good robustness under different remote-sensing scenarios. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A dual-branch detector framework is proposed for remote sensing incremental object detection, which decouples the learning pathways of old and new classes to alleviate inter-class confusion and catastrophic forgetting. A multi-granularity dynamic selection (MDS) strategy and a sigmoid-based DIST loss are introduced to improve old-class knowledge transfer by selecting informative teacher responses and preserving inter-class and intra-class relationships. What are the implications of the main findings? The proposed framework provides a practical solution for updating remote sensing object detectors when new object categories emerge, without requiring full retraining with all historical data. Experiments on DIOR and DOTA demonstrate that the method can achieve a favorable balance between old-class retention and new-class adaptation, indicating its potential for continual remote sensing image interpretation in evolving scenarios. In practical remote sensing object detection tasks, the application of deep learning approaches often takes the form of incremental learning: when the application includes new target types that were not encountered during training, a pre-trained model must acquire new knowledge without suffering catastrophic forgetting. Among the various techniques proposed, knowledge distillation (KD)-based regularization has proven to be one of the most effective methods. Current KD-based approaches primarily focus on addressing inter-task confusion and optimizing feature selection during distillation processes. In this paper, we propose a dual-branch detector-independent learning framework and a multi-granularity dynamic selection strategy. The former decouples detection tasks for old and new classes to mitigate inter-class confusion, while the latter is a novel, exquisitely designed distillation mechanism that ensures precise transfer of critical old-class information. Moreover, we apply a DIST loss that aligns both inter-class and intra-class relations, further enhancing the fidelity of old-class knowledge transfer. Experiments on the DIOR and DOTA datasets demonstrate that our method significantly outperforms state-of-the-art incremental-learning approaches for remote-sensing object detection and exhibits good robustness under different remote-sensing scenarios. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18122032 |
