| Summary: | With the continuous development of wind power generation technology and the continuous increase in the demand for electric energy, the height of the fan tower is more and more demanding. It is very important to detect the weld produced in the welding process of fan tower. In this paper, an algorithm for weld defect detection based on traditional image processing and convolutional neural network is proposed. Firstly, the traditional image processing algorithm is used to gray the weld image collected by industrial camera. Then, the gray image of welding seam is enhanced to improve the visual effect and clear the image, which is convenient for further processing and analysis of the image by computer. Finally, the image is used as the input of the trained convolution neural network to judge whether there are defects outside the weld.
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