4,002 research outputs found
PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation
Unsupervised pixel-level defective region segmentation is an important task
in image-based anomaly detection for various industrial applications. The
state-of-the-art methods have their own advantages and limitations:
matrix-decomposition-based methods are robust to noise but lack complex
background image modeling capability; representation-based methods are good at
defective region localization but lack accuracy in defective region shape
contour extraction; reconstruction-based methods detected defective region
match well with the ground truth defective region shape contour but are noisy.
To combine the best of both worlds, we present an unsupervised patch
autoencoder based deep image decomposition (PAEDID) method for defective region
segmentation. In the training stage, we learn the common background as a deep
image prior by a patch autoencoder (PAE) network. In the inference stage, we
formulate anomaly detection as an image decomposition problem with the deep
image prior and domain-specific regularizations. By adopting the proposed
approach, the defective regions in the image can be accurately extracted in an
unsupervised fashion. We demonstrate the effectiveness of the PAEDID method in
simulation studies and an industrial dataset in the case study
Ano-SuPs: Multi-size anomaly detection for manufactured products by identifying suspected patches
Image-based systems have gained popularity owing to their capacity to provide
rich manufacturing status information, low implementation costs and high
acquisition rates. However, the complexity of the image background and various
anomaly patterns pose new challenges to existing matrix decomposition methods,
which are inadequate for modeling requirements. Moreover, the uncertainty of
the anomaly can cause anomaly contamination problems, making the designed model
and method highly susceptible to external disturbances. To address these
challenges, we propose a two-stage strategy anomaly detection method that
detects anomalies by identifying suspected patches (Ano-SuPs). Specifically, we
propose to detect the patches with anomalies by reconstructing the input image
twice: the first step is to obtain a set of normal patches by removing those
suspected patches, and the second step is to use those normal patches to refine
the identification of the patches with anomalies. To demonstrate its
effectiveness, we evaluate the proposed method systematically through
simulation experiments and case studies. We further identified the key
parameters and designed steps that impact the model's performance and
efficiency.Comment: accepted oral presentation at the 18th INFORMS DMDA Worksho
Image-based Process Monitoring via Generative Adversarial Autoencoder with Applications to Rolling Defect Detection
abstract: Image-based process monitoring has recently attracted increasing attention due to the advancement of the sensing technologies. However, existing process monitoring methods fail to fully utilize the spatial information of images due to their complex characteristics including the high dimensionality and complex spatial structures. Recent advancement of the unsupervised deep models such as a generative adversarial network (GAN) and generative adversarial autoencoder (AAE) has enabled to learn the complex spatial structures automatically. Inspired by this advancement, we propose an anomaly detection framework based on the AAE for unsupervised anomaly detection for images. AAE combines the power of GAN with the variational autoencoder, which serves as a nonlinear dimension reduction technique with regularization from the discriminator. Based on this, we propose a monitoring statistic efficiently capturing the change of the image data. The performance of the proposed AAE-based anomaly detection algorithm is validated through a simulation study and real case study for rolling defect detection.Dissertation/ThesisMasters Thesis Industrial Engineering 201
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