Clear Memory-Augmented Auto-Encoder for Surface Defect Detection

• URL: http://arxiv.org/abs/2208.03879v1
• Date: Mon, 8 Aug 2022 02:39:03 GMT
• Title: Clear Memory-Augmented Auto-Encoder for Surface Defect Detection
• Authors: Wei Luo, Tongzhi Niu, Lixin Tang, Wenyong Yu, Bin Li
• Abstract summary: We propose a clear memory-augmented auto-encoder to repair abnormal foregrounds and preserve clear backgrounds. A general artificial anomaly generation algorithm is proposed to simulate anomalies that are as realistic and feature-rich as possible. At last, we propose a novel multi scale feature residual detection method for defect segmentation.
• Score: 10.829080460965478
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In surface defect detection, due to the extreme imbalance in the number of positive and negative samples, positive-samples-based anomaly detection methods have received more and more attention. Specifically, reconstruction-based methods are the most popular. However, exiting methods are either difficult to repair abnormal foregrounds or reconstruct clear backgrounds. Therefore, we propose a clear memory-augmented auto-encoder. At first, we propose a novel clear memory-augmented module, which combines the encoding and memory-encoding in a way of forgetting and inputting, thereby repairing abnormal foregrounds and preservation clear backgrounds. Secondly, a general artificial anomaly generation algorithm is proposed to simulate anomalies that are as realistic and feature-rich as possible. At last, we propose a novel multi scale feature residual detection method for defect segmentation, which makes the defect location more accurate. CMA-AE conducts comparative experiments using 11 state-of-the-art methods on five benchmark datasets, showing an average 18.6% average improvement in F1-measure.

Related papers

• Continuous Memory Representation for Anomaly Detection [24.58611060347548]
  CRAD is a novel anomaly detection method for representing normal features within a "continuous" memory. In an evaluation using the MVTec AD dataset, CRAD significantly outperforms the previous state-of-the-art method by reducing 65.0% of the error for multi-class unified anomaly detection.
  arXiv  Detail & Related papers  (2024-02-28T12:38:44Z)
• 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)
• AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [59.08735812631131]
  Anomaly inspection plays an important role in industrial manufacture. Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data. We propose AnomalyDiffusion, a novel diffusion-based few-shot anomaly generation model.
  arXiv  Detail & Related papers  (2023-12-10T05:13:40Z)
• Making Reconstruction-based Method Great Again for Video Anomaly Detection [64.19326819088563]
  Anomaly detection in videos is a significant yet challenging problem. Existing reconstruction-based methods rely on old-fashioned convolutional autoencoders. We propose a new autoencoder model for enhanced consecutive frame reconstruction.
  arXiv  Detail & Related papers  (2023-01-28T01:57:57Z)
• Reference-Based Autoencoder for Surface Defect Detection [7.163582730053925]
  We propose a novel unsupervised reference-based autoencoder (RB-AE) to accurately inspect a variety of textured defects. artificial defects and a novel pixel-level discrimination loss function are utilized for training to enable the model to obtain pixel-level discrimination ability.
  arXiv  Detail & Related papers  (2022-11-18T07:13:55Z)
• Visual Anomaly Detection Via Partition Memory Bank Module and Error Estimation [28.100204573591505]
  Reconstruction method based on the memory module for visual anomaly detection attempts to narrow the reconstruction error for normal samples while enlarging it for anomalous samples. This work proposes a new unsupervised visual anomaly detection method to jointly learn effective normal features and eliminate unfavorable reconstruction errors. To evaluate the effectiveness of the proposed method for anomaly detection and localization, extensive experiments are conducted on three widely-used anomaly detection datasets.
  arXiv  Detail & Related papers  (2022-09-26T06:15:47Z)
• Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection [122.4894940892536]
  We present a novel self-supervised masked convolutional transformer block (SSMCTB) that comprises the reconstruction-based functionality at a core architectural level. In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.
  arXiv  Detail & Related papers  (2022-09-25T04:56:10Z)
• A Feature Memory Rearrangement Network for Visual Inspection of Textured Surface Defects Toward Edge Intelligent Manufacturing [4.33060257697635]
  We propose an unsupervised feature memory rearrangement network (FMR-Net) to accurately detect various textural defects simultaneously. We use artificial synthetic defects to enable the model to recognize anomalies, while traditional wisdom relies only on defect-free samples. FMR-Net exhibits state-of-the-art inspection accuracy and shows great potential for use in edge-computing-enabled smart industries.
  arXiv  Detail & Related papers  (2022-06-22T04:05:13Z)
• Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection [97.93062818228015]
  We propose to integrate the reconstruction-based functionality into a novel self-supervised predictive architectural building block. Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field. We demonstrate the generality of our block by integrating it into several state-of-the-art frameworks for anomaly detection on image and video.
  arXiv  Detail & Related papers  (2021-11-17T13:30:31Z)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)
• Memory Augmented Generative Adversarial Networks for Anomaly Detection [12.341523221155708]
  Memory Augmented Generative Adrial Networks (MEMGAN) interacts with a memory module for both the encoding and generation processes. Our algorithm is such that most of the textitencoded normal data are inside the convex hull of the memory units, while the abnormal data are isolated outside. Decoded memory units in MEMGAN are more interpretable and disentangled than previous methods, which further demonstrates the effectiveness of the memory mechanism.
  arXiv  Detail & Related papers  (2020-02-07T08:46:26Z)

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.