先验知识辅助的金属涂层损伤分割方法OACSTPCD

Automatic recognition of metal coating defects has significant value in realistic applications.As deep learning makes breakthrough in surface defect segmentation for a variety of materials,most of deep convolutional neural network segmentation models are trained in an end-to-end manner.However,it is difficult to exploit prior knowledge about metal coating defects in end-to-end deep learning and adapt to the variable scale of the defects and the limited training data.This paper proposes a defect segmentation algorithm based on prior knowledge about metal coating defects to unify U-Net,a deep learning segmentation model for automatic metal coating defect recognition.This anomaly segmentation is based on Hue channel distribution and edge response.Being trained in a knowledge driven manner,the model can exclude outliers from training data and effectively avoid over-fitting.On a metal coating defect image dataset with four defect types,including crack,blister,rusting and flaking,the proposed method achieves 81.24% mIoU,which is advantageous over end-to-end deep learning.The experiment shows that knowledge-driven model can boost the performance of deep learning models in metal coating defect segmentation.