Bilateral guidance network for one-shot metal defect segmentation

Metal defect inspection is critical for maintaining product quality and ensuring production safety. However, the vast majority of existing defect segmentation methods rely heavily on large-scale datasets that only cater to specific defects, making them unsuitable for the industrial sector, where training samples are often limited. To address these challenges, we propose a bilateral guidance network for one-shot metal defect segmentation that leverages the perceptual consistency of background regions within industrial images to distinguish foreground and background regions. Our model uses an interactive feature reweighting scheme that models the inter- and self-dependence of foreground and background feature maps, enabling us to build robust pixel-level correspondences. Our proposed method demonstrates good domain adaptability and accurately segments defects in multiple materials, such as steel, leather, and carpet, among others. Additionally, we have incorporated a multi-scale receptive field encoder to enhance the model's ability to perceive objects of varying scales, providing a comprehensive solution for industrial defect segmentation. Experimental results indicate that our proposed method has the potential to be effective in a variety of real-world applications where defects may not be immediately visible or where large amounts of labeled data are not readily available. With only one shot, our method achieves the state-of-the-art performance of 41.62% mIoU and 70.30% MPA on the Defect-3ⁱ dataset.