A Surface Defect Detection Method for Sheet Metal Based on Mixed Texture Features
At present,it is difficult to meet customer requirements for surface defect detection of sheet metal using traditional manual inspection.Therefore,this article proposes a surface defect detection algorithm based on mixed texture features,which can accurately and robustly detect whether there are defects in the surface image of the sheet metal.The texture feature items were effectively combined using a mixed feature vector based on fusion,and the image texture was extracted using the gray level co-occurrence matrix method.Train and detect the sample set using BP artificial neural network.This method can accurately detect surface defects on the board,with an average detection success rate of 97.2%.This detection method meets the requirements of enterprises,improves detection efficiency,and reduces costs.This detection technology is worth applying and promoting.
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