高效遥感图像目标检测：算法与模型规模双视角的深度网络压缩与加速技术进展

王柳谦; 张菁; 赵一; 谢笑阳; 卓力

doi:10.12263/DZXB.20251009

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高效遥感图像目标检测：算法与模型规模双视角的深度网络压缩与加速技术进展

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- 高效遥感图像目标检测：算法与模型规模双视角的深度网络压缩与加速技术进展
- Efficient Object Detection in Remote Sensing Images: Advances in Deep Network Compression and Acceleration from Algorithmic and Model-Scale Perspectives
- 电子学报 2026年54卷第3期页码：1348-1363
- 作者机构：
  
  1.北京工业大学信息科学技术学院，北京 100124
  2.郑州大学网络空间安全学院，河南郑州 450002
  3.计算智能与智能系统北京市重点实验室，北京 100124
  4.北京工业大学计算机学院，北京 100124
- 作者简介：
  
  王柳谦男，1997年1月生，河南平顶山人。北京工业大学信息科学技术学院博士研究生，现为郑州大学网络空间安全学院讲师。主要研究方向为遥感影像目标检测与识别等。E-mail: wangliuqian@zzu.edu.cn
  张菁女，1975年2月生，广东梅县人。博士，现为北京工业大学信息科学技术学院教授、博士生导师。主要研究方向为图像处理、图像识别、图像检索等。E-mail: zhj@bjut.edu.cn
  赵一女，2000年8月生，河南许昌人。现为北京工业大学信息科学技术学院博士研究生。主要研究方向为目标检测与图像处理等。 E-mail: ZZYY-@emails.bjut.edu.cn
  谢笑阳女，1991年2月生，湖南宁远人。博士，现为北京工业大学计算机学院讲师、研究生导师。主要研究方向为图像处理、人工智能、遥感图像处理以及目标识别等。 E-mail: xiexiaoyang@bjut.edu.cn
  卓力女，1971年10月生，江苏徐州人。博士，现为北京工业大学信息科学技术学院教授、博士生导师。主要研究方向为图像/视频的编码与传输、多媒体大数据处理等。 E-mail: zhuoli@bjut.edu.cn
- 基金信息：
  
  北京市自然科学基金(L247025)
- DOI：10.12263/DZXB.20251009
  中图分类号： TP751.1;
- 收稿：2025-12-11，
  
  录用：2026-01-13，
  
  纸质出版：2026-03-25
- 稿件说明：
移动端阅览
王柳谦, 张菁, 赵一, 等. 高效遥感图像目标检测：算法与模型规模双视角的深度网络压缩与加速技术进展[J]. 电子学报, 2026, 54(03): 1348-1363.

WANG Liuqian, ZHANG Jing, ZHAO Yi, et al. Efficient Object Detection in Remote Sensing Images: Advances in Deep Network Compression and Acceleration from Algorithmic and Model-Scale Perspectives[J]. Acta Electronica Sinica, 2026, 54(03): 1348-1363.
王柳谦, 张菁, 赵一, 等. 高效遥感图像目标检测：算法与模型规模双视角的深度网络压缩与加速技术进展[J]. 电子学报, 2026, 54(03): 1348-1363. DOI：10.12263/DZXB.20251009

WANG Liuqian, ZHANG Jing, ZHAO Yi, et al. Efficient Object Detection in Remote Sensing Images: Advances in Deep Network Compression and Acceleration from Algorithmic and Model-Scale Perspectives[J]. Acta Electronica Sinica, 2026, 54(03): 1348-1363. DOI：10.12263/DZXB.20251009

摘要

近年来，卷积神经网络（Convolutional Neural Network，CNN）、视觉Transformer以及大规模语言模型（Large Language Model，LLM）等深度学习模型在表示学习与任务泛化方面取得显著进展。然而，随着模型参数规模与计算复杂度迅速增加，其推理延迟与存储开销持续攀升。在遥感领域，高分辨率遥感图像与合成孔径雷达（Synthetic Aperture Radar，SAR）图像具有幅面大、分辨率高、场景复杂、目标多尺度以及小目标占比高等特点。因此，目标检测模型不仅需具备强大的表征能力以应对复杂背景与尺度变化，还必须满足星载、机载及边缘设备等受限平台对实时性与低功耗的严格要求，这使得“精度提升”与“高效部署”之间的矛盾日益凸显。本文首先系统回顾了深度网络压缩与加速技术的发展历程，涵盖从传统手工优化到剪枝、量化与知识蒸馏等经典方法，并进一步探讨了大模型时代下的条件计算与推理优化策略，包括混合专家系统（Mixture of Experts，MoE）、键值（Key Value，KV）缓存优化与混合量化等。其次，通过整合遥感目标检测领域的主流数据集与评价指标体系，本文构建了一个面向“精度-效率-资源”权衡的多维评估基准，用于分析不同压缩方案在遥感场景中的适配性与局限性。与现有综述多沿单一算法主线或仅聚焦轻量网络不同，本文从“算法视角”与“模型规模视角”两个互补维度组织研究脉络：算法视角侧重于在既定模型上通过剪枝、量化、蒸馏与稀疏化来降低计算与存储复杂度；模型规模视角则区分轻量模型与大规模基础模型在结构冗余、敏感模块与内存瓶颈方面的差异，进而分析压缩策略在不同体量模型中的作用机制与优化潜力。再次，基于这一双视角框架，本文对代表性方法的核心思想与实现要点进行对比，总结影响压缩效果的关键因素，并提炼可复用的工程化实践建议，以期为模型选型与部署配置提供参考。最后，结合遥感智能解译的应用需求与绿色低碳发展趋势，本文展望自适应压缩、多模态协同压缩、参数高效微调与软硬件协同优化等方向，为遥感目标检测模型在边缘端与工程系统中的高效落地提供系统化的理论支持与实践指导。

Abstract

In recent years

deep learning models such as convolutional neural networks (CNNs)

vision transformers

and large language models (LLMs) have made remarkable breakthroughs in representation learning and task generalization. However

this progress has been accompanied by rapid increase in parameter scale and computational complexity

leading to rising inference latency and storage overhead. In remote sensing applications

high-resolution remote sensing images and synthetic aperture radar (SAR) images are characterized by large coverage

high resolution

complex scenes

and multi-scale variations with a high proportion of small objects. Object detection models must therefore possess strong representational capability to handle complex backgrounds and scale variations

while also meeting stringent real-time and low-power constraints on resource-limited platforms such as satellite-borne

airborne

and edge devices. As a result

the tension between “improving accuracy” and “efficient deployment” has become increasingly pronounced. This study systematically reviews the development of deep network compression and acceleration techniques

covering traditional manual optimizations as well as classical methods such as pruning

quantization

and knowledge distillation. It further discusses conditional computation and inference optimization in the era of large models

including mixture-of-experts (MoE) architectures

key-value (KV) cache optimization

and hybrid quantization. By integrating mainstream datasets and evaluation metrics in the field of remote sensing object detection

this paper establishes a multi-dimensional evaluation benchmark oriented toward the “accuracy-efficiency-resource” trade-offs

aimed at characterizing the applicability and limitations of different compression approaches in remote sensing scenarios. Unlike existing surveys that often follow a single algorithmic thread or focus solely on lightweight networks

this review organizes the research landscape from two complementary perspectives: the algorithmic perspective and the model-scale perspective. The former emphasizes reducing computational and storage complexity through pruning

quantization

distillation

and sparsification on a given model. The latter distinguishes between lightweight models and large-scale foundation in terms of structural redundancy

sensitive modules

and memory bottlenecks

analyzing how compression strategies operate and where further optimization is possible across models of different sizes. Based on this dual-perspective framework

the paper compares and analyzes the core ideas and implementation details of representative methods

summarizes key factors influencing compression performance

and extracts reusable engineering practices to inform model selection and deployment configurations. Finally

considering the application demands of remote sensing intelligent interpretation and the trend toward green and low-carbon computing

this paper looks ahead to promising directions such as adaptive compression

multimodal collaborative-compression

parameter-efficient fine-tuning

and hardware-software co-optimization. The aim is to provide systematic reference and theoretical support for the efficient deployment of remote sensing object detection models on edge devices and in engineering systems.

关键词

Keywords

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