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Defect Detection Method For Steel Based On Semantic Segmentation

The traditional methods used to detect defects in steel have always had many problems, such as long time consuming, causing a certain degree of damage to the surface of steel, and consuming more human resources. With the development of deep learning, theoretically, through semantic segmentation and image classification technology, deep neural network can be used to detect whether there are defects in steel, the categories of defects contained, and the locations of the defects, thereby solving the problems of traditional detection methods. The problem is destructive and time-consuming. Most recent researches on related aspects are mostly experiments with small data sets and small samples, and have achieved good performance. In this large-scale data set, based on the structure used by U-Net, the new network design is newly designed. For PSD U-Net, the model uses hole convolution and HDC design instead of the original convolution layer to preserve the picture details to the greatest extent under the premise of expanding the receptive field. The parametric sigmoid norm (PSN) layer is superimposed on the feature extraction network to further Improve recognition rate. Finally, the paper is based on three networks, U-Net, Mask R-CNN and PSD U-Net, and continuously transforms the feature extraction network for experiments. The results show that the PSD U-Net architecture and the network with superimposed PSN layers have better results for the rest of the isometric models.