Fully Convolutional Networks for Surface Defect Inspection in Industrial Environment

In this paper, we propose a reusable and high-efficiency two-stage deep learning based method for surface defect inspection in industrial environment. Aiming to achieve trade-offs between efficiency and accuracy simultaneously, our method makes a novel combination of a segmentation stage (stage1) and a detection stage (stage2), which are consisted of two fully convolutional networks (FCN) separately. In the segmentation stage we use a lightweight FCN to make a spatially dense pixel-wise prediction to inference the area of defect coarsely and quickly. Those predicted defect areas act as the initialization of stage2, guiding the process of detection to refine the segmentation results. We also use an unusual training strategy: training with the patches cropped from the images. Such strategy has greatly utility in industrial inspection where training data may be scarce. We will validate our findings by analyzing the performance obtained on the dataset of DAGM 2007.