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机器视觉检测面临的十大挑战(一)

时间:2018-03-09  来源:本文搜集于网络,由机器视觉检测设备厂家创盈时代整理,欢迎转载,转载请注明出  点击:261

机器视觉检测可以改善自动化设置。集成的机器人解决方案可以快速轻松地提供机器视觉检测的优势,无需编程技巧。但是,即使技术有所改进,视觉也是机器人技术的一个比较“棘手”的问题,这里有10个总结出来的机器视觉检测挑战。
Machine vision testing can improve automation settings. The integrated robot solution can quickly and easily provide the advantage of machine vision detection without the need of programming skills. However, even with the improvement of technology, vision is a more difficult problem of robotics. There are 10 summaries of machine vision detection challenges.

机器视觉检测系统最常见的功能是检测已知物体的位置和方向
The most common function of a machine vision detection system is to detect the position and direction of a known object

1.照明(lighting)

如果有过在低光照下拍摄数码照片的经验,就会知道照明至关重要。糟糕的照明会毁掉一切。成像传感器不像人眼那样适应性强或敏感。如果照明类型错误,视觉传感器将无法可靠地检测到物体。
If you have the experience of shooting digital photos under low light, it is important to know that lighting is important. Bad lighting will destroy everything. Imaging sensors are not as adaptable or sensitive as the human eye. If the lighting type is wrong, the visual sensor will not be able to detect the object reliably.

有各种克服照明挑战的方法。一种方法是将有源照明结合到视觉传感器本身中。其他解决方案包括使用红外照明,环境中的固定照明或使用其他形式的光的技术,例如激光。

There are various ways to overcome the lighting challenges. One way is to combine active lighting into the visual sensor itself. Other solutions include infrared lighting, fixed lighting in the environment, or the use of other forms of light, such as lasers.


视觉检测设备


2.变形或铰接(deformed or articulated)

球是用计算机视觉设置来检测的简单对象。你可能只是检测它的圆形轮廓,也许使用模板匹配算法。但是,如果球被压扁,它会改变形状,同样的方法将不再起作用。这是变形。它会导致一些机器视觉检测技术相当大的问题。
A ball is a simple object that is detected by computer vision. You may just check the round outline of it, perhaps using a template matching algorithm. But if the ball is squashed, it will change the shape, and the same method will not work again. This is a deformation. It will lead to considerable problems in machine vision detection techniques.

铰接类似,是指由可移动关节引起的变形。例如,当您在肘部弯曲手臂时,手臂的形状会发生变化。各个链接(骨骼)保持相同的形状,但轮廓变形。由于许多视觉算法使用形状轮廓,因此清晰度使得物体识别更加困难。
Hinged similar, refers to the deformation caused by a movable joint. For example, when you bend your arm at the elbow, the shape of the arm changes. Each link (bone) keeps the same shape, but the contour is deformed. Because many visual algorithms use shape contour, the definition makes the object recognition more difficult.

3.职位和方向(position and direction)

机器视觉检测系统最常见的功能是检测已知物体的位置和方向。因此,大多数集成视觉解决方案通常都克服了这两者面临的挑战。
The most common function of a machine vision detection system is to detect the position and direction of a known object. As a result, most integrated visual solutions often overcome the challenges they face.

只要整个物体可以在摄像机图像内被查看,检测物体的位置通常是直截了当的。许多系统对于对象方向的变化也是强健的。但是,并不是所有的方向都是平等的。虽然检测沿一个轴旋转的物体是足够简单的,但是检测物体何时3D旋转则更为复杂。
As long as the whole object can be viewed in the camera image, the location of the object is usually straightforward. Many systems are also robust to changes in the direction of the object. But not all directions are equal. Although it is simple enough to detect objects rotated along an axis, it is more complex to detect when the object is rotated by 3D.

4.背景(background)

图像的背景对物体检测的容易程度有很大的影响。想象一个极端的例子,对象被放置在一张纸上,在该纸上打印同一对象的图像。在这种情况下,机器视觉检测设置可能不可能确定哪个是真实的物体。
The background of the image has a great influence on the degree of the object detection. Imagine an extreme example where the object is placed on a piece of paper and printed on the paper to print the image of the same object. In this case, the machine vision detection setup may not be possible to determine which is a real object.

完美的背景是空白的,并提供与检测到的物体良好的对比。它的确切属性将取决于正在使用的视觉检测算法。如果使用边缘检测器,那么背景不应该包含清晰的线条。背景的颜色和亮度也应该与物体的颜色和亮度不同。
The perfect background is blank and provides a good contrast to the objects detected. Its exact properties will depend on the visual detection algorithms being used. If the edge detector is used, the background should not contain clear lines. The color and brightness of the background should also be different from the color and brightness of the object.

以上是关于机器视觉检测面临的十大挑战上半部分的介绍,在下次我们将继续解读机器视觉检测面临的十大挑战的下半部分,更多机器视觉方面的技术资讯请关注深圳市创盈时代科技有限公司。

本文关键字:机器视觉检测

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