Guided Hybrid Quantization for Object Detection in Remote Sensing Imagery via One-to-One Self-Teaching

Zhang, J; Lei, J; Xie, W; Li, Y; Yang, G; Jia, X

Guided Hybrid Quantization for Object Detection in Remote Sensing Imagery via One-to-One Self-Teaching

Zhang, J Lei, J

Xie, W Li, Y Yang, G Jia, X

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Geoscience and Remote Sensing, 2023, 61
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Zhang, J
dc.contributor.author	Lei, J https://orcid.org/0000-0003-0851-6565
dc.contributor.author	Xie, W
dc.contributor.author	Li, Y
dc.contributor.author	Yang, G
dc.contributor.author	Jia, X
dc.date.accessioned	2024-04-29T09:33:33Z
dc.date.available	2024-04-29T09:33:33Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Geoscience and Remote Sensing, 2023, 61
dc.identifier.issn	0196-2892
dc.identifier.issn	1558-0644
dc.identifier.uri	http://hdl.handle.net/10453/178482
dc.description.abstract	Deep convolutional neural networks (CNNs) have improved remote sensing image analysis, but their high computational demands may limit their deployment on low-end devices with limited resources, such as intelligent satellites and unmanned aerial vehicles. Considering the computation complexity, we propose a guided hybrid quantization with one-to-one self-teaching (GHOST) framework. More concretely, we first design a structure called guided quantization self-distillation (GQSD), an innovative idea for realizing a lightweight model through the synergy of quantization and distillation. The training process of the quantization model is guided by its full-precision model, which is time-saving and cost-saving without preparing a huge pretrained model in advance. Second, we put forward a hybrid quantization (HQ) module that automatically acquires the optimal bit-width by imposing a threshold constraint on the distribution distance between the center point and samples in the weight search space, aiming to retain more shallow detail information that is advantageous for small object detection. Third, to improve information transformation, we propose a one-to-one self-teaching (OST) module to give the student network the ability to self-judgment. A switch control machine (SCM) builds a bridge between the student and teacher networks in the same location to help the teacher reduce wrong guidance and impart vital knowledge about objects without vast background information to the student. This distillation method allows a model to learn from itself and gain substantial improvement without any additional supervision. Extensive experiments on a multimodal dataset (VEDAI) and single-modality datasets (DOTA, NWPU, and DIOR) show that object detection based on GHOST outperforms the existing detectors. The tiny parameters (< 9.7 MB) and bit-operations (BOPs) (< 2158 G) compared with any remote sensing-based, lightweight, or distillation-based algorithms demonstrate the superiority in the lightweight design domain. Our code and model will be released at https://github.com/icey-zhang/GHOST.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Geoscience and Remote Sensing
dc.relation.isbasedon	10.1109/TGRS.2023.3293147
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0404 Geophysics, 0906 Electrical and Electronic Engineering, 0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.subject.classification	37 Earth sciences
dc.subject.classification	40 Engineering
dc.title	Guided Hybrid Quantization for Object Detection in Remote Sensing Imagery via One-to-One Self-Teaching
dc.type	Journal Article
utslib.citation.volume	61
utslib.for	0404 Geophysics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-29T09:33:16Z
pubs.publication-status	Published
pubs.volume	61

Abstract:

Deep convolutional neural networks (CNNs) have improved remote sensing image analysis, but their high computational demands may limit their deployment on low-end devices with limited resources, such as intelligent satellites and unmanned aerial vehicles. Considering the computation complexity, we propose a guided hybrid quantization with one-to-one self-teaching (GHOST) framework. More concretely, we first design a structure called guided quantization self-distillation (GQSD), an innovative idea for realizing a lightweight model through the synergy of quantization and distillation. The training process of the quantization model is guided by its full-precision model, which is time-saving and cost-saving without preparing a huge pretrained model in advance. Second, we put forward a hybrid quantization (HQ) module that automatically acquires the optimal bit-width by imposing a threshold constraint on the distribution distance between the center point and samples in the weight search space, aiming to retain more shallow detail information that is advantageous for small object detection. Third, to improve information transformation, we propose a one-to-one self-teaching (OST) module to give the student network the ability to self-judgment. A switch control machine (SCM) builds a bridge between the student and teacher networks in the same location to help the teacher reduce wrong guidance and impart vital knowledge about objects without vast background information to the student. This distillation method allows a model to learn from itself and gain substantial improvement without any additional supervision. Extensive experiments on a multimodal dataset (VEDAI) and single-modality datasets (DOTA, NWPU, and DIOR) show that object detection based on GHOST outperforms the existing detectors. The tiny parameters (< 9.7 MB) and bit-operations (BOPs) (< 2158 G) compared with any remote sensing-based, lightweight, or distillation-based algorithms demonstrate the superiority in the lightweight design domain. Our code and model will be released at https://github.com/icey-zhang/GHOST.

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http://hdl.handle.net/10453/178482