DefFiller: Mask-Conditioned Diffusion for Salient Steel Surface Defect Generation
Journal:
arXiv
Published Date:
Dec 20, 2024
Abstract
Current saliency-based defect detection methods show promise in industrial
settings, but the unpredictability of defects in steel production environments
complicates dataset creation, hampering model performance. Existing data
augmentation approaches using generative models often require pixel-level
annotations, which are time-consuming and resource-intensive. To address this,
we introduce DefFiller, a mask-conditioned defect generation method that
leverages a layout-to-image diffusion model. DefFiller generates defect samples
paired with mask conditions, eliminating the need for pixel-level annotations
and enabling direct use in model training. We also develop an evaluation
framework to assess the quality of generated samples and their impact on
detection performance. Experimental results on the SD-Saliency-900 dataset
demonstrate that DefFiller produces high-quality defect images that accurately
match the provided mask conditions, significantly enhancing the performance of
saliency-based defect detection models trained on the augmented dataset.
