Deep Learning-Based Generic Automatic Surface Defect Inspection (ASDI) With Pixelwise Segmentation

Abstract

Automatic surface defect inspection (ASDI) is a crucial and challenging problem in industry because it affects the quality and efficiency of production greatly. Deep learning-based methods achieve promising improvements for surface detection, but they rely on a massive training data set that is impractical in industry. In this study, we propose a generic method that works effectively even on the small size training data set. Especially, we introduce a ResMask generative adversarial network (GAN) framework that is a residual GAN to expand the insufficient defect data sets. Meanwhile, the existing inspection data sets are so much easier to detect than the data sets in the real industrial scenarios that new industrial surface defect data sets containing more diverse and challenging images are established. Then, a coarse-to-fine module (CFM) that consists of a coarse detection subnetwork and a fine segmentation subnetwork is proposed for the needs of fast detection for high-resolution images. In the coarse detection stage, spatial pyramid pooling (SPP) is utilized to increase the receptive field of the network, reduce the false detection rate, and determine the approximate location of defects. In the fine segmentation stage, the receptive field of the network is enlarged by atrous SPP (ASPP), and skip links that incorporate low-level with high-level features achieve pixelwise precision on defect segmentation. Finally, our algorithm has achieved state-of-the-art results in DAGM, HR, and WB data sets (0.859, 0.761, and 0.805, respectively) according to MIoU. It achieves an average processing time of 44.4 ms on the test images with a resolution of $512 \\,\\,\\times \\,\\, 512$ and 131.6 ms for $2048 \\times 2048$ images. On the DAGM data set, the detection accuracy of mean intersection over union (MIoU) reaches 0.859.