汽车行业的机器视觉

在过去的几年里,Machine Vision在零售和制造等动态行业中获得了巨大的普及。这些行业正在利用该技术来提高客户经验,优化资源的使用,并实现更好的质量保证。manbetx登陆到目前为止,我们都知道有4.0个承诺提供的福利行业。行业4.0是一种伞术语,指的是产业价值链过程中发生的众多发展。这些变化主要由新兴技术,尤其是云,提供更好的方法来组织和管理制造业内的所有标准流程。

In the United States, Japan, South Korea, Germany, and other European countries, the automotive industry plays a significant part in the overall manufacturing sector. In 2019 alone, almost 92 million motor vehicles were produced worldwide. Theautomobile industryis highly automated for mass production, with strict quality requirements and a high degree of cost sensitivity. Large manufacturers place a premium on having a close and trust-based collaborative relationship with their suppliers and technology providers who support this high degree of automation. Machine vision is a crucial part of this highly automated sector.

在汽车行业中,质量检查和保证是机器视觉可以证明是最有帮助的领域之一。

Related ArticleMachine Vision is creating a new wave in the Automobile Industry

HUMAN MISTAKES HAPPEN

这种机器愿景能力的一个主要例子是为了克服人类局限性是质量控制,错误可能导致产品有缺陷的产品,冲动的订单,甚至更糟糕的声誉。人类传统上有质量控制,因为它需要判断。这个单位上的油漆工作是否没有任何缺陷?这个特殊的部分是否有或没有任何耀眼的缺陷,这可能对用户来说是危险的?当用孤立的案例呈现时,人类可以很容易地制作该确定。

However, an interesting thing happens when a human views not just a single unit but hundreds of them, let alone thousands of units streaming along a high-speed assemble line. After repeatedly seeing an image, that image ends up being imprinted on the brain. So when an inspector sees a number of parts at the proper quality level and then sees a unit that’s defective, the inspector’s eyes send that signal to the brain — but the brain may instead use the imprinted image of a flawless piece and not register a problem.

这是机器愿景可以让工作更容易的地方。分类可以实现上述问题的准确性和一致的结果。分类涉及预测项目属于哪个类或类别。一些分类器输出二进制分类,如是/否。有些是多级,可以将项目分为几个可能的类别之一。分类是深度学习分类算法的一个非常常见的用例用于解决工业制造环境中的问题分类,图像识别和基于图像的分类。在分类问题中,输入通常是需要归类的项目的图像。该算法处理整个图像并基于之前的培训准确地对其进行分类。

质量检查中有用的应用

The key examples of image processing systems in the automotive industry include:

1. Engine Character Recognition

In thisapplication机器视觉用于捕获标记在引擎上的部件号的图像,并使用OCR工具读取它们。可以准确地阅读部件号而不会受到标记质量的影响。这可以防止不同类型的发动机混合。此应用程序消除了手动检查的繁琐任务。

2. Autoparts Classification

由于制造过程包括大范围的物品,因此根据不同的汽车模型的产生部件的分类可以证明是一个繁琐的任务。为了解决这个问题,可以部署分类算法以识别不同的模型类型并在没有任何人为干预的情况下分离相同的分离。

3. Sticker Classification

Classification of stickers并根据变体的手动分离贴纸,传统上手动完成。这个过程通常是耗时的,需要大量的劳动力,并具有很大的错误可能性。使用OCR工具和机器视觉可以实现高水平的精度和效率。

4.焊接检查

Solder inspection has traditionally been difficult with 2D cameras. 3D cameras can measure height, so solder can be inspected accurately with machine vision algorithms. Using the height extraction function the3D height images can be converted into gray-scale images (mm → shade) to generate a cross-sectional image at a specified height. Using the cross-sectional area and shape of the image helps in achieving stable fillet inspection.

还阅读与丰田生产系统集成机器视觉和AI

CONCLUSION

As the industry keeps growing, new tools for machine vision will be needed as traditional solutions begin to reach their limits in some areas. In recent years, new inspection tasks have surfaced. Deep learning-based machine vision systems will provide a new type of tool that can fill in some of the foreseeable gaps of manufacturing inspection.

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