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Energy-Constrained Model Pruning for Efficient In-Orbit Object Detection in Optical Remote Sensing Images

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Space Information Networks (SINC 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2057))

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Abstract

Efficient 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.

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Acknowledgments

This work is supported by National Natural Science Foundation of China under Grant 41901376, Hubei Provincial Natural Science Foundation of China under Grant 2022CFB989, and Foundation for the National Key Laboratory under Grant 6142217210503, and China University of Geosciences (Wuhan) Teaching Laboratory Open Foundation SKJ2022230.

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Authors and Affiliations

  1. National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan, 430033, China

    Shaohua Qiu & Xinghua Xu

  2. School of Computer Science, China University of Geosciences, Wuhan, 430074, China

    Du Chen & Jia Liu

  3. East Lake Laboratory, Wuhan, 430202, China

    Shaohua Qiu

Authors
  1. Shaohua Qiu
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  2. Du Chen
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  3. Xinghua Xu
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  4. Jia Liu
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Corresponding author

Correspondence to Shaohua Qiu .

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Editors and Affiliations

  1. Institute of China Electronic Equipment System Engineering Corporation, Beijing, China

    Quan Yu

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Qiu, S., Chen, D., Xu, X., Liu, J. (2024). Energy-Constrained Model Pruning for Efficient In-Orbit Object Detection in Optical Remote Sensing Images. In: Yu, Q. (eds) Space Information Networks. SINC 2023. Communications in Computer and Information Science, vol 2057. Springer, Singapore. https://doi.org/10.1007/978-981-97-1568-8_4

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  • DOI: https://doi.org/10.1007/978-981-97-1568-8_4

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  • Online ISBN: 978-981-97-1568-8

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