Semi-Supervised Defect Detection via Conditional Diffusion and CLIP-Guided Noise Filtering

• URL: http://arxiv.org/abs/2507.05588v1
• Date: Tue, 08 Jul 2025 01:53:34 GMT
• Title: Semi-Supervised Defect Detection via Conditional Diffusion and CLIP-Guided Noise Filtering
• Authors: Shuai Li, Shihan Chen, Wanru Geng, Zhaohua Xu, Xiaolu Liu, Can Dong, Zhen Tian, Changlin Chen,
• Abstract summary: This paper introduces a semi-supervised defect detection framework based on conditional diffusion (DSYM)<n>A conditional diffusion model synthesizes multi-scale pseudo-defect samples, while a CLIP cross-modal feature-based noise filtering mechanism mitigates label contamination.<n>This research provides a high-precision, low-labeling-dependent solution for defect detection in industrial quality inspection scenarios.
• Score: 8.132909775584395
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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% mAP@0.5 with the same amount of labeled data as traditional supervised methods, and 75.1% mAP@0.5 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.

Related papers

• LR-IAD:Mask-Free Industrial Anomaly Detection with Logical Reasoning [1.3124513975412255]
  Industrial Anomaly Detection (IAD) is critical for ensuring product quality by identifying defects.<n>Existing vision-language models (VLMs) and Multimodal Large Language Models (MLLMs) address some limitations but rely on mask annotations.<n>We propose a reward function that dynamically prioritizes rare defect patterns during training to handle class imbalance.
  arXiv  Detail & Related papers  (2025-04-28T06:52:35Z)
• A Hybrid Framework for Statistical Feature Selection and Image-Based Noise-Defect Detection [55.2480439325792]
  This paper presents a hybrid framework that integrates both statistical feature selection and classification techniques to improve defect detection accuracy.<n>We present around 55 distinguished features that are extracted from industrial images, which are then analyzed using statistical methods.<n>By integrating these methods with flexible machine learning applications, the proposed framework improves detection accuracy and reduces false positives and misclassifications.
  arXiv  Detail & Related papers  (2024-12-11T22:12:21Z)
• Trimming the Risk: Towards Reliable Continuous Training for Deep Learning Inspection Systems [10.121053770426759]
  The industry increasingly relies on deep learning (DL) technology for manufacturing inspections. finite labeled datasets often fail to encompass all natural variations. We develop a robust CT-based maintenance approach that updates DL models using reliable data selections.
  arXiv  Detail & Related papers  (2024-09-13T15:02:13Z)
• Unlearnable Examples Detection via Iterative Filtering [84.59070204221366]
  Deep neural networks are proven to be vulnerable to data poisoning attacks. It is quite beneficial and challenging to detect poisoned samples from a mixed dataset. We propose an Iterative Filtering approach for UEs identification.
  arXiv  Detail & Related papers  (2024-08-15T13:26:13Z)
• Leveraging Latent Diffusion Models for Training-Free In-Distribution Data Augmentation for Surface Defect Detection [9.784793380119806]
  We introduce DIAG, a training-free Diffusion-based In-distribution Anomaly Generation pipeline for data augmentation. Unlike conventional image generation techniques, we implement a human-in-the-loop pipeline, where domain experts provide multimodal guidance to the model. We demonstrate the efficacy and versatility of DIAG with respect to state-of-the-art data augmentation approaches on the challenging KSDD2 dataset.
  arXiv  Detail & Related papers  (2024-07-04T14:28:52Z)
• MAPL: Memory Augmentation and Pseudo-Labeling for Semi-Supervised Anomaly Detection [0.0]
  A new meth-odology for detecting surface defects in in-dustrial settings is introduced, referred to as Memory Augmentation and Pseudo-Labeling(MAPL)<n>The methodology first in-troduces an anomaly simulation strategy, which significantly improves the model's ability to recognize rare or unknown anom-aly types.<n>An end-to-end learning framework is employed by MAPL to identify the abnormal regions directly from the input data.
  arXiv  Detail & Related papers  (2024-05-10T02:26:35Z)
• Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
  We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets. We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
  arXiv  Detail & Related papers  (2024-01-06T07:30:41Z)
• Imbalanced Data Classification via Generative Adversarial Network with Application to Anomaly Detection in Additive Manufacturing Process [5.225026952905702]
  This paper proposes a novel data augmentation method based on a generative adversarial network (GAN) using additive manufacturing process image sensor data. The diverse and high-quality generated samples provide balanced training data to the classifier. The effectiveness of the proposed method is validated by both open-source data and real-world case studies in polymer and metal AM processes.
  arXiv  Detail & Related papers  (2022-10-28T16:08:21Z)
• Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection [81.07346419422605]
  Anomaly detection aims at identifying deviant samples from the normal data distribution. Contrastive learning has provided a successful way to sample representation that enables effective discrimination on anomalies. We propose a novel hierarchical semi-supervised contrastive learning framework, for contamination-resistant anomaly detection.
  arXiv  Detail & Related papers  (2022-07-24T18:49:26Z)
• Anomaly Detection Based on Selection and Weighting in Latent Space [73.01328671569759]
  We propose a novel selection-and-weighting-based anomaly detection framework called SWAD. Experiments on both benchmark and real-world datasets have shown the effectiveness and superiority of SWAD.
  arXiv  Detail & Related papers  (2021-03-08T10:56:38Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.