Local and global feature fusion network for surface defect segmentation

Abstract

Surface defect detection is an indispensable part of industrial production in order to guarantee product quality. With rapid development of deep learning, automatic surface defect detection is gradually applied to a variety of industrial scenarios. However, defect detection still faces some challenges, such as diverse defect types, various defect size and texture structures. To address the problems, we proposed a local and global feature fusion network (LGFNet) for surface defect segmentation. The network adopts a U-shaped encoder-decoder structure with a convolution-based local feature extraction unit (LFE) and a transformer-based global feature extraction unit (GFE). LFE utilizes multi-head convolutional attention to obtain the detailed textures of defects, and GFE utilizes dual attention module to obtain global contextual information of defects. LGFNet cross-cascades the two feature extraction units to obtain multi-scale defect features, thus adapting the segmentation network to different types of defects. Experiments on two widely used surface defect datasets (NEU-Seg, Road Defect) demonstrate that the network can accurately segment defects of multiple shapes and sizes.