ALMRR: Anomaly Localization Mamba on Industrial Textured Surface with Feature Reconstruction and Refinement

• URL: http://arxiv.org/abs/2407.17705v1
• Date: Thu, 25 Jul 2024 01:58:10 GMT
• Title: ALMRR: Anomaly Localization Mamba on Industrial Textured Surface with Feature Reconstruction and Refinement
• Authors: Shichen Qu, Xian Tao, Zhen Qu, Xinyi Gong, Zhengtao Zhang, Mukesh Prasad,
• Abstract summary: Unsupervised anomaly localization on industrial textured images has achieved remarkable results. Image-based methods tend to reconstruct both normal and anomalous regions well. Feature-based methods contain a large amount of semantic information.
• Score: 3.417713976280609
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsupervised anomaly localization on industrial textured images has achieved remarkable results through reconstruction-based methods, yet existing approaches based on image reconstruction and feature reconstruc-tion each have their own shortcomings. Firstly, image-based methods tend to reconstruct both normal and anomalous regions well, which lead to over-generalization. Feature-based methods contain a large amount of distin-guishable semantic information, however, its feature structure is redundant and lacks anomalous information, which leads to significant reconstruction errors. In this paper, we propose an Anomaly Localization method based on Mamba with Feature Reconstruction and Refinement(ALMRR) which re-constructs semantic features based on Mamba and then refines them through a feature refinement module. To equip the model with prior knowledge of anomalies, we enhance it by adding artificially simulated anomalies to the original images. Unlike image reconstruction or repair, the features of synthesized defects are repaired along with those of normal areas. Finally, the aligned features containing rich semantic information are fed in-to the refinement module to obtain the anomaly map. Extensive experiments have been conducted on the MVTec-AD-Textured dataset and other real-world industrial dataset, which has demonstrated superior performance com-pared to state-of-the-art (SOTA) methods.

Related papers

• Multi-feature Reconstruction Network using Crossed-mask Restoration for Unsupervised Industrial Anomaly Detection [4.742650815342744]
  Unsupervised anomaly detection is of great significance for quality inspection in industrial manufacturing. We propose a multi-feature reconstruction network, MFRNet, using crossed-mask restoration in this paper. Our method is highly competitive with or significantly outperforms other state-of-the-arts on four public available datasets and one self-made dataset.
  arXiv  Detail & Related papers  (2024-04-20T05:13:56Z)
• Reconstruction-Based Anomaly Localization via Knowledge-Informed Self-Training [10.565214056914174]
  knowledge-informed self-training (KIST) integrates knowledge into reconstruction model through self-training. KIST utilizes weakly labeled anomalous samples in addition to the normal ones and exploits knowledge to yield pixel-level pseudo-labels of the anomalous samples.
  arXiv  Detail & Related papers  (2024-02-22T03:15:13Z)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction [75.91471250967703]
  We introduce a novel sampling framework called Steerable Conditional Diffusion. This framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement. We achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities.
  arXiv  Detail & Related papers  (2023-08-28T08:47:06Z)
• Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization [5.101905755052051]
  Anomaly detection has a wide range of applications and is especially important in industrial quality inspection. Reconstruction-based methods use reconstruction errors to detect anomalies without considering positional differences between samples. In this study, a reconstruction-based method using the noise-to-norm paradigm is proposed, which avoids the invariant reconstruction of anomalous regions.
  arXiv  Detail & Related papers  (2023-07-06T08:06:48Z)
• 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)
• Editing Out-of-domain GAN Inversion via Differential Activations [56.62964029959131]
  We propose a novel GAN prior based editing framework to tackle the out-of-domain inversion problem with a composition-decomposition paradigm. With the aid of the generated Diff-CAM mask, a coarse reconstruction can intuitively be composited by the paired original and edited images. In the decomposition phase, we further present a GAN prior based deghosting network for separating the final fine edited image from the coarse reconstruction.
  arXiv  Detail & Related papers  (2022-07-17T10:34:58Z)
• 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)
• Spatially-Adaptive Image Restoration using Distortion-Guided Networks [51.89245800461537]
  We present a learning-based solution for restoring images suffering from spatially-varying degradations. We propose SPAIR, a network design that harnesses distortion-localization information and dynamically adjusts to difficult regions in the image.
  arXiv  Detail & Related papers  (2021-08-19T11:02:25Z)

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.