基于云边协同的计算机视觉推理机制

唐博恒; 柴鑫刚

doi:10.11959/j.issn.1000-0801.2021107

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基于云边协同的计算机视觉推理机制

专题：通信与AI融合 | 更新时间：2024-06-05

- 基于云边协同的计算机视觉推理机制
- Cloud-edge collaboration based computer vision inference mechanism
- 电信科学 2021年37卷第5期页码：72-81
- 作者机构：
- 作者简介：
  
  [ "唐博恒（1992− ），男，中国移动通信有限公司研究院工程师，主要研究方向为智慧城市、视频监控、计算机视觉、图像算法与算法的应用落地" ]
  [ "柴鑫刚（1976− ），男，中国移动通信有限公司研究院高级工程师，主要研究方向为视频云、计算机视觉、智慧城市等相关的关键技术研究与产品创新" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2021107
  中图分类号： TP393
- 网络出版日期：2021-05，
  
  纸质出版日期：2021-05-20
- 稿件说明：
移动端阅览
唐博恒, 柴鑫刚. 基于云边协同的计算机视觉推理机制[J]. 电信科学, 2021,37(5):72-81.

Boheng TANG, Xingang CHAI. Cloud-edge collaboration based computer vision inference mechanism[J]. Telecommunications science, 2021, 37(5): 72-81.
唐博恒, 柴鑫刚. 基于云边协同的计算机视觉推理机制[J]. 电信科学, 2021,37(5):72-81. DOI： 10.11959/j.issn.1000-0801.2021107.

Boheng TANG, Xingang CHAI. Cloud-edge collaboration based computer vision inference mechanism[J]. Telecommunications science, 2021, 37(5): 72-81. DOI： 10.11959/j.issn.1000-0801.2021107.

摘要

深度学习和云计算的普及推动了计算机视觉在各行业中的广泛应用。但集中化的云端推理服务存在带宽资源消耗大、图像数据隐私泄露、时效性难以满足等问题，难以充分满足计算机视觉在行业应用上的多样化应用需求。而通信网络的双吉比特升级将促进视觉算法云边算法深层次协同。对基于云边协同的计算机视觉推理机制开展研究。首先对近年主流的云侧和边缘侧计算机视觉推理模型的优劣势进行了分析和阐述，然后在此基础上对云边协同计算机视觉推理模型框架、部署机制等开展研究，详细讨论模型分布式推理模型分割策略，云边协同网络部署优化策略。最后通过数据协同、网络分区协同、业务功能协同 3 方面对云边协同深度推理未来的发展挑战进行了展望。

Abstract

The popularity of deep learning and cloud computing has promoted the widespread application of computer vision in various industries.However

centralized cloud inference services have problems such as high bandwidth resource consumption

image data privacy leakage

and high latency.It is hard that satisfy demand which requires diversified computer vision application.The dual gigabit upgrade of the communication network will promote depth collaboration of computer vision cloud-edge algorithms.Aiming to study the computer vision inference mechanism based on cloud-edge collaboration.Firstly

the advantages and disadvantages of the mainstream cloud and edge computer vision inference models in recent years were analyzed and explained

and on this basis

research on the cloud-edge collaborative computer vision inference model framework and deployment mechanism was carried out

model distributed reasoning model segmentation strategy

cloud-side collaborative network deployment optimization strategy was discussed in detail.In the end

the challenge and prospect of deep learning cloud-edge collaboration inference in future was discussed through data collaboration

network partition collaboration

and business function collaboration .

关键词

Keywords

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