FDSNeT: An Accurate Real-Time Surface Defect Segmentation Network
Jian Zhang, Runwei Ding, Miaoju Ban, Tianyu Guo
Abstract
Surface defect detection is a common task for industrial quality control, which increasingly requires accuracy and real-time ability. However, the current segmentation networks are not effective in dealing with defect boundary details, local similarity of different defects and low contrast between defect and background. To this end, we propose a real-time surface defect segmentation network (FDSNet) based on two-branch architecture, in which two corresponding auxiliary tasks are introduced to encode more boundary details and semantic context. To handle the local similarity problem of different surface defects, we propose a Global Context Upsampling (GCU) module by capturing long-range context from multi-scales. Moreover, we present a representative Mobile phone screen Surface Defect (MSD) segmentation dataset to alleviate the lack of dataset in this field. Experiments on NEU-Seg, Magnetic-tile-defect-datasets and MSD dataset show that the proposed FDSNet achieves promising trade-off between accuracy and inference speed. The dataset and code are available at https://github.com/jianzhang96/fdsnet.