Improving Impulse Noise Classification Using Ensemble Learning Methods

Kumarasamy, Kunaraj; Wenisch, S. Maria; Balaji, S.; Suriya, L. J. Jenifer; Jerlin, A.; Rajkumar, S

doi:10.1007/978-981-33-4299-6_16

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…The collected fabric dataset undergoes an initial data integrity check to identify and address any corrupted pixels, ensuring data quality [11,12]. Subsequently, a series of preprocessing steps are applied to prepare the dataset for use in training and testing a fabric defect detection system.…”

Section: Methodsmentioning

confidence: 99%

ResNet50: Automated Fabric Defect Detection and Classification based on a Deep Learning Approach

K. Sharath Kumar, Dr. M. Rama Bai

2023

tjjpt

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"The demand for an intelligent computer-based system for visual inspection is on the rise in the textile business, especially among those who place a premium on textile quality. In this research, we present a fully automated AI-driven algorithm for defect detection in fabric, one that makes use of deep neural network models that have already been pre-trained. In order to train the networks, the fabric images go through a series of pre-processing steps where typical image processing techniques are applied. In order to train and categorize various fabric flaws, we use Deep Convolutional Neural Networks (DCNNs) and the pre-trained ResNeT network. The system obtains a best classification accuracy of 96.40% in simulations utilizing preexisting textile datasets. This model's detection and classification system can help human operators spot flaws in the fabric manufacturing process with this degree of precision."

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Section: Methodsmentioning

confidence: 99%

ResNet50: Automated Fabric Defect Detection and Classification based on a Deep Learning Approach

K. Sharath Kumar, Dr. M. Rama Bai

2023

tjjpt

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“…The collected fabric dataset is initially checked for any corrupted pixels and it is further pre-processed [15,16]. Figure 3…”

Section: Methodsmentioning

confidence: 99%

Automated Fabric Defect Detection and Classification: A Deep Learning Approach

Sandhya¹,

Sashikumar²,

Priyanka³

et al. 2021

TLR

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A computer-based intelligent visual inspection system plays a major role in evaluating the quality of textile fabrics and its demand is continuously increasing in the textile industry, especially when the quality of textile is to be considered. In this paper, we propose an AI-based automated fabric defect detection algorithm which utilizes pre-trained deep neural network models for classifying possible fabric defects. The fabric images are enhanced by pre-processing at various levels using conventional image processing techniques and they are used to train the networks. The Deep Convolutional Neural Network (DCNN) and a pre-trained network, AlexNet, are used to train and classify various fabric defects. With the exiting textile dataset, a maximum classification accuracy of 92.60% is achieved in the conducted simulations. With this accuracy, the detection and classification system based on this classifier model can aid the human to find faults in the fabric manufacturing unit.

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“…Ensemble learning is a growing topic of interest in the field of pattern recognition and machine learning. It has attracted great attention, as it can significantly improve the accuracy and generalizability of learning systems [8,9]. In general, ensemble learning is divided into bagging, boosting and stacking and each of these three stages use algorithm designs based on the tree model.…”

Section: Introductionmentioning

confidence: 99%

A Novel Active Noise Control Method Based on Variational Mode Decomposition and Gradient Boosting Decision Tree

et al. 2023

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Diversified noise sources pose great challenges in the engineering of an ANC (active noise control) system design. To solve this problem, this paper proposes an ANC method based on VMD (variational mode decomposition) and Ensemble Learning. VMD is used to extract IMFs (Intrinsic Model Functions) of different types of noise and obtain the approximate entropy of each IMF. Clustering analysis on the output of VMD is conducted based on the PCA (principal component analysis) dimension reduction method and k-means++ method to get classification results for different noises. On the basis of the clustering results, different GBDT (gradient boosting decision tree) regressors are constructed for different noise types, in order to create a high-performance ANC system for multiple noise sources. To verify the effectiveness of the proposed method, this paper designed four simulation schemes for the ANC: obstacle-free rectangular enclosed space, rectangular enclosed space with obstacle, obstacle-free trapezoidal enclosed space and trapezoidal enclosed space with obstacle. When machine gun noise is used as an example, noise attenuation by the proposed method in four simulation schemes is −23.27 dB, −21.6 dB, −19.08 dB and −15.48 dB respectively.

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Improving Impulse Noise Classification Using Ensemble Learning Methods

Cited by 4 publications

References 14 publications

ResNet50: Automated Fabric Defect Detection and Classification based on a Deep Learning Approach

ResNet50: Automated Fabric Defect Detection and Classification based on a Deep Learning Approach

Automated Fabric Defect Detection and Classification: A Deep Learning Approach

A Novel Active Noise Control Method Based on Variational Mode Decomposition and Gradient Boosting Decision Tree

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