Unsupervised anomaly detection of MEMS in low illumination based on polarimetric Support Vector Data Description

Huang, Yaokang; Sang, Mei; Xing, Lun; Hu, Haofeng; Liu, Tiegen

doi:10.1364/oe.438564

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…However, the MSA methods may not function because the collected process data present characteristics such as strong nonlinearity and non-Gaussian distribution. Recently, with the improvement of computing hardware, deep learning based on deep networks has achieved great success in computer vision fields such as face recognition and target detection [14][15][16][17][18][19]. Due to the powerful capabilities of nonlinear data processing and deep feature learning, many abnormal data detection approaches using deep learning have been widely applied in industrial processes.…”

Section: Introductionmentioning

confidence: 99%

Abnormal data detection for industrial processes using adversarial autoencoders support vector data description

Qiu

Song

Wang

2022

Meas. Sci. Technol.

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Abnormal data detection for industrial processes is essential in industrial process monitoring and is an important technology to ensure production safety. However, for most industrial processes, it is a challenge to establish an effective abnormal data detection model due to the following issues: (1) weak model performance due to the small amount of process data; (2) trade-offs between model sparsity and accuracy; and (3) weak generalization ability of abnormal data detection model. To address these issues, a method based on adversarial autoencoders support vector data description (AAESVDD) is presented in this work. First, a novel construction strategy is designed for a hybrid feature dataset based on the adversarial autoencoder (AAE). The hybrid feature dataset utilizes the latent feature and reconstruction residual extracted by the AAE to enhance the feature diversity of the process data. Then, combining the support vector data description (SVDD) and Bayesian optimization algorithm (BOA), an automatic detection model for abnormal data of the hybrid feature dataset is established. Meanwhile, a BOA objective function based on the criterion of the hybrid risk minimization (HRM) is proposed to automatically optimize the model parameters, which further enhances the generalization ability of the SVDD-based model. Finally, the effectiveness of the proposed AAESVDD method is illustrated with the UCI benchmark datasets and an industrial penicillin fermentation process.

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Section: Introductionmentioning

confidence: 99%

Abnormal data detection for industrial processes using adversarial autoencoders support vector data description

Qiu

Song

Wang

2022

Meas. Sci. Technol.

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Polarized image super-resolution via a deep convolutional neural network

Yang

Li³

et al. 2023

Opt. Express

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Reduced resolution of polarized images makes it difficult to distinguish detailed polarization information and limits the ability to identify small targets and weak signals. A possible way to handle this problem is the polarization super-resolution (SR), which aims to obtain a high-resolution polarized image from a low-resolution one. However, compared with the traditional intensity-mode image SR, the polarization SR is more challenging because more channels and their nonlinear cross-links need to be considered as well as the polarization and intensity information need to be reconstructed simultaneously. This paper analyzes the polarized image degradation and proposes a deep convolutional neural network for polarization SR reconstruction based on two degradation models. The network structure and the well-designed loss function have been verified to effectively balance the restoration of intensity and polarization information, and can realize the SR with a maximum scaling factor of four. Experimental results show that the proposed method outperforms other SR methods in terms of both quantitative evaluation and visual effect evaluation for two degradation models with different scaling factors.

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LVTSR: learning visible image texture network for infrared polarization super-resolution imaging

Wang,

Chen,

Peng

et al. 2024

Opt. Express

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Infrared polarization (IRP) division-of-focal-plane (DoFP) imaging technology has gained attention, but limited resolution due to sensor size hinders its development. High-resolution visible light (VIS) images are easily obtained, making it valuable to use VIS images to enhance IRP super-resolution (SR). However, IRP DoFP SR is more challenging than infrared SR due to the need for accurate polarization reconstruction. Therefore, this paper proposes an effective multi-modal SR network, integrating high-resolution VIS image constraints for IRP DoFP image reconstruction, and incorporating polarization information as a component of the loss function to achieve end-to-end IRP SR. For the multi-modal IRP SR, a benchmark dataset was created, which includes 1559 pairs of registered images. Experiments on this dataset demonstrate that the proposed method effectively utilizes VIS images to restore polarization information in IRP images, achieving a 4x magnification. Results show superior quantitative and visual evaluations compared to other methods.

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Unsupervised anomaly detection of MEMS in low illumination based on polarimetric Support Vector Data Description

Cited by 3 publications

References 21 publications

Abnormal data detection for industrial processes using adversarial autoencoders support vector data description

Abnormal data detection for industrial processes using adversarial autoencoders support vector data description

Polarized image super-resolution via a deep convolutional neural network

LVTSR: learning visible image texture network for infrared polarization super-resolution imaging

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