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Energy-Constrained Model Pruning for Efficient In-Orbit Object Detection in Optical Remote Sensing Images
Qiu, Shaohua1,3; Chen, Du2; Xu, Xinghua1; Liu, Jia2
2024
Source PublicationCommunications in Computer and Information Science
Volume2057 CCIS
Pages34-49
Original Document TypeConference article (CA)
AbstractEfficient object detection from optical remote sensing (RS) images has always been an important interpretation task for in-orbit RS applications. In recent years, convolutional neural networks have been widely used for object detection with significantly improved detection accuracy. However, the large detection models pose great challenges for the computing, memory and energy supply of resource-constrained in-orbit platforms. In this paper, we propose an efficient in-orbit object detection method with low memory, computation and energy requirements. The proposed method first integrates the compact modules of GhostNet into the detector and further performs the L1-norm based filter pruning to significantly reduce model size and computational complexity. Besides, we propose to use energy as a key metric in filter pruning, and present a novel energy-guided layer-wise pruning rate estimation method so as to achieve energy-efficient object detection. Comprehensive experiments have shown the effectiveness of the proposed method in terms of model size, computational complexity, latency and energy consumption, while maintaining comparable detection accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
KeywordComplex networks Computational complexity Convolutional neural networks Energy efficiency Energy utilization Object recognition Optical remote sensing Orbits Constrained resources Efficient object detections Filter pruning In-orbit In-orbit object detection Lightweight CNN Objects detection Optical remote sensing Optical remote sensing image Remote sensing images
DOI10.1007/978-981-97-1568-8_4
Language英语
ISSN1865-0929
Indexed ByEI
EI Accession Number20241715950888
PublisherSpringer Science and Business Media Deutschland GmbH
EISSN1865-0937
Conference Name7th International Conference on Space Information Network, SINC 2023
EI KeywordsObject detection
Conference DateOctober 12, 2023 - October 13, 2023
Conference PlaceWuhan, China
Citation statistics
Cited Times [WOS]:-1   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.cug.edu.cn/handle/2XU834YA/360987
Collection中国地质大学(武汉)
Corresponding AuthorQiu, Shaohua
Affiliation1.National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan; 430033, China
2.School of Computer Science, China University of Geosciences, Wuhan; 430074, China
3.East Lake Laboratory, Wuhan; 430202, China
Recommended Citation
GB/T 7714
Qiu, Shaohua,Chen, Du,Xu, Xinghua,et al. Energy-Constrained Model Pruning for Efficient In-Orbit Object Detection in Optical Remote Sensing Images[C]:Springer Science and Business Media Deutschland GmbH,2024:34-49.
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