دورية أكاديمية
Target Detection Model Distillation Using Feature Transition and Label Registration for Remote Sensing Imagery
| العنوان: | Target Detection Model Distillation Using Feature Transition and Label Registration for Remote Sensing Imagery |
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| المؤلفون: | Boya Zhao, Qing Wang, Yuanfeng Wu, Qingqing Cao, Qiong Ran |
| المصدر: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 5416-5426 (2022) |
| بيانات النشر: | IEEE, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Ocean engineering LCC:Geophysics. Cosmic physics |
| مصطلحات موضوعية: | Deep neural network, feature transition, label registration, model distillation, remote sensing, target detection, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809 |
| الوصف: | Deep convolution networks have been widely used in remote sensing target detection for various applications in recent years. Target detection models with many parameters provide better results but are not suitable for resource-constrained devices due to their high computational cost and storage requirements. Furthermore, current lightweight target detection models for remote sensing imagery rarely have the advantages of existing models. Knowledge distillation can improve the learning ability of a small student network from a large teacher network due to acceleration and compression. However, current knowledge distillation methods typically use mature backbones as teacher and student networks are unsuitable for target detection in remote sensing imagery. In this article, we propose a target detection model distillation (TDMD) framework using feature transition and label registration for remote sensing imagery. A lightweight attention network is designed by ranking the importance of the convolutional feature layers in the teacher network. Multiscale feature transition based on a feature pyramid is utilized to constrain the feature maps of the student network. A label registration procedure is proposed to improve the TDMD model's learning ability of the output distribution of the teacher network. The proposed method is evaluated on the DOTA and NWPU VHR-10 remote sensing image datasets. The results show that the TDMD achieves a mean Average Precision (mAP) of 75.47% and 93.81% on the DOTA and NWPU VHR-10 datasets, respectively. Moreover, the model size is 43% smaller than that of the predecessor model (11.8 MB and 11.6 MB for the two datasets). |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2151-1535 |
| Relation: | https://ieeexplore.ieee.org/document/9815508/; https://doaj.org/toc/2151-1535 |
| DOI: | 10.1109/JSTARS.2022.3188252 |
| URL الوصول: | https://doaj.org/article/2103c0c89ebb4878ad9c2ebd0fd06040 |
| رقم الأكسشن: | edsdoj.2103c0c89ebb4878ad9c2ebd0fd06040 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 21511535 |
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| DOI: | 10.1109/JSTARS.2022.3188252 |