Semi-Supervised Defect Detection via Conditional Diffusion and CLIP-Guided Noise Filtering
Journal:
arXiv
Published Date:
Jul 8, 2025
Abstract
In the realm of industrial quality inspection, defect detection stands as a
critical component, particularly in high-precision, safety-critical sectors
such as automotive components aerospace, and medical devices. Traditional
methods, reliant on manual inspection or early image processing algorithms,
suffer from inefficiencies, high costs, and limited robustness. This paper
introduces a semi-supervised defect detection framework based on conditional
diffusion (DSYM), leveraging a two-stage collaborative training mechanism and a
staged joint optimization strategy. The framework utilizes labeled data for
initial training and subsequently incorporates unlabeled data through the
generation of pseudo-labels. A conditional diffusion model synthesizes
multi-scale pseudo-defect samples, while a CLIP cross-modal feature-based noise
filtering mechanism mitigates label contamination. Experimental results on the
NEU-DET dataset demonstrate a 78.4% [email protected] with the same amount of labeled
data as traditional supervised methods, and 75.1% [email protected] with only 40% of the
labeled data required by the original supervised model, showcasing significant
advantages in data efficiency. This research provides a high-precision,
low-labeling-dependent solution for defect detection in industrial quality
inspection scenarios. The work of this article has been open-sourced at
https://github.com/cLin-c/Semisupervised-DSYM.
