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          迪奧科技標語
          On the deep learning algorithm of machine vision
          Author:Administrator   Published in:2019-12-06 15:09

          The working process of machine vision is inseparable from deep learning. Deep learning is a new field in the research of machine vision. Its core lies in the establishment and Simulation of neural network of human brain for analysis and learning. It imitates the mechanism of human brain to analyze data, such as text, voice and image. The concept of deep learning originates from the research of artificial intelligence neural network.

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          The general workflow of deep learning can be summarized as annotation, training and reasoning. First, collect and collect images manually, mark features, and form data; then, submit these data to the computer for training, generate a network for evaluation, if the performance of the network meets the requirements, it can be online to achieve detection. After the network goes online, a lot of data will be generated. At the same time, these data can become new samples. By adding the method of data iterative optimization, the network and detection system will become better and better.

          Building a high quality training data set is the key to deep learning. For the successful deployment of deep learning solutions, high-quality training data sets are essential. Edge conditions or improperly marked data sets make the network chaotic, while well marked and internally consistent data sets have better effects. Training images must be typical in the categories they represent, and the training image style must be as close as possible to the images that will be encountered when the system is deployed.

          The application of deep learning to machine vision can be roughly divided into three types: one is classification, i.e. products can be divided into qualified and unqualified, which is the largest application of deep learning; the other is positioning, i.e. to help users locate the location and quantity of objects; the third is segmentation, so as to find out the contour of defects, and make a more precise judgment of products based on the contour and size of defects 。

          Compared with traditional machine learning, deep learning plays a more significant role in machine vision. In dealing with irregular images, deep learning machine vision solutions, even if the images are complex, can automatically learn the features of defects through deep learning algorithm, making the analysis of irregular images possible; while in traditional machine vision solutions, when the images are irregular and irregular, the features of defects are difficult to be set manually, and the images cannot be analyzed.

          In terms of accuracy, the deep learning machine vision solution can improve the accuracy of detection through deep learning algorithm and manufacturing specific data; if the defect part of the traditional machine vision solution is slightly different from the previously set defect, the traditional vision cannot detect such defect, resulting in low accuracy of detection.

          Although deep learning has many advantages, not all tasks are applicable. Deep learning can provide a very convenient solution for the strong subjectivity or qualitative problems. The subjective problems or the problems that get the answers from the complex interaction of many conditions are ideal applications. However, deep learning is not beneficial to all tasks. Many basic inspection tasks are suitable for traditional machine vision technology, such as existence or lack of clearly defined features, measurement and alignment.

          The above is about how machine vision works. I hope it can help you better understand machine vision.


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          亚洲国产剧情中文视频在线
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