How to get started with deep learning in machine vision While deep learning techniques offer efficiency and speed advantages over rule-based techniques, starting a project can be daunting.

Mike Fussell

首页> 外文期刊>Vision Systems Design >How to get started with deep learning in machine vision While deep learning techniques offer efficiency and speed advantages over rule-based techniques, starting a project can be daunting.

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How to get started with deep learning in machine vision While deep learning techniques offer efficiency and speed advantages over rule-based techniques, starting a project can be daunting.

机译：如何开始使用机器视觉进行深度学习虽然深度学习技术比基于规则的技术具有效率和速度优势，但开始一个项目可能令人生畏。

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Problems with high variability or subjectivity can be difficult to solve with traditional rules-based machine vision techniques. A seemingly simple problem like grading produce relies on a complex network of interactions between subjective and highly-variable criteria including size, shape, color, and uniformity. By training a neural network with examples of each grade, developers can use deep learning to accomplish such a task.

机译：具有高可变性或主观性的问题可能难以通过传统的基于规则的机器视觉技术来解决。看起来很简单的问题（如分级产品）取决于主观条件和高度可变的标准（包括大小，形状，颜色和均匀性）之间相互作用的复杂网络。通过使用每个年级的示例训练神经网络，开发人员可以使用深度学习来完成此类任务。

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《Vision Systems Design》 |2019年第10期|20-22|共3页
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Mike Fussell;
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How to get started with deep learning in machine vision While deep learning techniques offer efficiency and speed advantages over rule-based techniques, starting a project can be daunting.

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