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    Talking about edge detection in machine vision
    Author:Administrator   Published in:2019-12-31 15:45

    Machine vision has the advantages of high speed, high accuracy, repeatability, objectivity, etc. After automatic equipment is added to machine vision, the efficiency and accuracy of detection and assembly will be significantly improved compared to human labor. One of the hot spots. Edge detection is an indispensable link in machine vision and an important image preprocessing technology.

    Because the edges are the result of discontinuity in gray values, such discontinuities can often be easily detected using derivatives, and first and second derivatives are generally selected to detect edges. In machine vision detection, this method is usually called edge detection local operator method. For image edge detection, the Canny algorithm is used to process and detect the image. The basic steps of the algorithm are as follows:


    Edge detection

    1. Filtering: The edge detection algorithm is mainly based on the first and second derivatives of the image intensity, but the calculation of the derivatives is very sensitive to noise, so a filter must be used to improve the performance of the noise-related edge detector. It should be pointed out that most filters cause loss of edge strength while reducing noise. Therefore, there is a trade-off between enhancing edges and reducing noise.

    2. Enhancement: The basis for enhancing the edge is to determine the change in the intensity of the neighborhood of each point in the image. The enhancement algorithm can highlight points with significant changes in neighborhood (or local) intensity values. Edge enhancement is usually done by calculating the magnitude of the gradient.

    3. Detection: There are many points in the image with large gradient amplitudes, and these points are not all edges in a specific application field, so some method should be used to determine which points are edge points. The simplest edge detection criterion is the gradient amplitude threshold criterion.

    4. Positioning: If an edge position is required in an application, the position of the edge can be estimated at the sub-pixel resolution, and the position of the edge can also be estimated.

    In edge detection algorithms, the first three steps are very common. This is because in most cases, it is only necessary for the edge detector to indicate that the edge appears near a certain pixel in the image, and it is not necessary to indicate the exact position or direction of the edge.

    These four steps are essential when using machine vision for dimensional measurements, and in particular the precise location and orientation of the edges must be indicated. Machine vision inspection technology, with its powerful performance advantages, standardizes product quality, fast detection speed, reliable, stable and long-term detection results, and is widely used in various fields.

    The above is the introduction of edge detection in machine vision. Do you have a new understanding of machine vision? If you want to learn more about machine vision, welcome to follow our TEO technology. In addition, you have this need You can call us at any time to get in touch with us. We will provide you with good quality products and sincere service. We look forward to cooperating with you!

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