Analysis of Soft Intelligent Edge Computing Technologies for Unmanned Systems

Kaige ZHANG; Zhigang LU; Tianchang NIE; Zhiwei LI; Yuqiang GUO

doi:10.12382/bgxb.2022.1166

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Analysis of Soft Intelligent Edge Computing Technologies for Unmanned Systems

更新时间：2025-08-18

- Analysis of Soft Intelligent Edge Computing Technologies for Unmanned Systems
- Acta Armamentarii Vol. 44, Issue 9, Pages: 2611-2621(2023)
- 作者机构：
  
  北方自动控制技术研究所, 山西太原 030006
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2022.1166
  CLC：
- Received：30 November 2022，
  
  Published Online：25 September 2023，
  
  Published：20 September 2023
- 稿件说明：
移动端阅览
Kaige ZHANG, Zhigang LU, Tianchang NIE, et al. Analysis of Soft Intelligent Edge Computing Technologies for Unmanned Systems[J]. Acta Armamentarii, 2023, 44(9): 2611-2621.
DOI：

Kaige ZHANG, Zhigang LU, Tianchang NIE, et al. Analysis of Soft Intelligent Edge Computing Technologies for Unmanned Systems[J]. Acta Armamentarii, 2023, 44(9): 2611-2621. DOI： 10.12382/bgxb.2022.1166.

摘要

轻量化神经网络模型的设计及其在边缘端的部署是实现无人装备智能化的关键技术。从构造轻量级深度神经网络的角度出发

研究面向无人装备嵌入式平台应用的智能边缘计算软技术

重点分析模型剪枝、知识蒸馏和参数量化等方法

并以目标识别为例进行各类智能边缘计算技术性能分析

结合各类轻量化模型设计方法的优缺点

提出一种边缘计算的处理框架

即通过模型压缩方法来设计轻量化的神经网络模型

通过引入知识蒸馏的方法对轻量化模型进行有效训练

通过参数量化来加速模型推理时间。随着参数量化和知识蒸馏算法的成熟

该框架正逐渐变得有效可行

为智能边缘计算技术在无人化装备上的应用提供了技术参考。

Abstract

The design and deployment of light-weight neural network models are crucial for intelligent weapon systems. This paper explores the application of intelligent edge computing in unmanned systems from the perspective of building a light-weight deep neural network model

specifically focusing on parameter pruning

knowledge distillation

and parameter quantization techniques. Recent advancements in these fields are discussed

and the performance of various intelligent edge computing technologies is evaluated using object recognition as an example. Based on the advantages and disadvantages of each light-weight design method

a new framework for edge computing is proposed. With improvements in parameter quantization accuracy and introduction of knowledge distillation

the proposed framework becomes feasible for implementation. This approach provides valuable insights for the utilization of intelligent edge computing technologies in enhancing the military intelligence of unmanned systems.

关键词

Keywords

references

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