市場調查報告書

智慧製造的深度學習型機械視覺

Deep Learning-Based Machine Vision In Smart Manufacturing

出版商 ABI Research 商品編碼 737673
出版日期 內容資訊 英文 14 Pages
商品交期: 最快1-2個工作天內
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智慧製造的深度學習型機械視覺 Deep Learning-Based Machine Vision In Smart Manufacturing
出版日期: 2018年11月01日內容資訊: 英文 14 Pages
簡介

機械視覺對工業製造來說不可或缺,其功能,在品質檢驗,物體,及缺點的識別等,各種情形下被展開。近幾年,因為在機械視覺為基礎的SLAM (Simultaneous Localization And Mapping) 中發揮重要作用,成為自主式行動機器人發展也不可或缺之物。

本報告提供智慧製造的深度學習型機械視覺的相關調查,傳統技術與深度學習型技術比較,主要的利用案例,深度學習型機械視覺的可能性,及主要企業等相關之詳細分析。

目錄

  • 用語、定義
  • 傳統機械視覺技術
  • 傳統 vs. 深度學習型技術
  • 主要的利用案例
  • 深度學習型機械視覺的市場潛在性
  • 主要的新興企業
  • 主要的相關利益者
  • 主要建議、結論
目錄
Product Code: PT-2126

Machine vision has been a staple for industrial manufacturing. The capability is deployed in various scenarios, including quality inspection, object, and defect identification. In recent years, machine vision has also been crucial in the rise of the autonomous mobile robot, as machine vision plays a key role in visual based simultaneous localization and mapping.

With the rise of deep learning, more and more machine vision models are built on deep learning techniques, predominantly on convolutional neural networks. By building on convolutional neural networks, the accuracy and capabilities of deep learning based machine vision will improve as more data are gathered and used to train the model. One of the main verticals that are looking to adopt deep learning based machine vision is the manufacturing industry. As the manufacturing industry starts to embark on the journey of digital transformation, the adoption of deep learning based machine vision is expected to grow. Instrumental, Landing.ai and micropsi industries are among the main startups that are offering this technology.

At the moment, the implementation of deep learning based machine vision will rely heavily on edge device and on-premise servers. As the technology is still in its early stage, implementers need to be aware of several requirements prior to deployment. The training and testing of deep learning based machine vision models and algorithms require high definition data and high computational power, which is currently lacking in the manufacturing environment. It is also critical for the models to be interoperable with existing infrastructure, such as industrial-grade camera from Cognex, Basler and Keyence, and industrial cloud platform from industrial players like GE, ABB, SAP, PTC and Siemens. Redundancy, data privacy and sovereignty requirements should not be overlooked as well.

Table of Contents

  • Terminology and Definition
  • Conventional Machine Vision Technology
  • Conventional versus Deep Learning-Based Technology
  • Key Use Cases
  • Market Potential for Deep Learning-Based Machine Vision
  • Key Startups
  • Key Stakeholders
  • Key Recommendations and Conclusions
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