Fabric defect detection algorithm based on MFS and SVM

Zhao, Cuifang; Ma, Jiacheng

doi:10.1117/12.2513987

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…In the early research, the model can be divided into two parts: feature extractor and classifier. Where the feature extractor extracts the features of the defect image and the classifier takes them as input to derive classification results, Zhao [4] constructed a fabric defect detection model using a multiple fractal spectrum feature extractor with SVM as classifier. Earlier defect detection methods were not robust, slow and hard to detect complex defects.…”

Section: Surface Defect Detectionmentioning

confidence: 99%

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A Semi-Supervised Method for Steel Surface Defect Detection Based on Soft-teacher

2023

AJCIS

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Metal products are indispensable raw materials for society nowadays. Surface defects is inevitably occur during the production process, so defect detection is an important method to improve product quality. For the problem of lack of annotation of defect images in new production lines and low efficiency of manual annotation, we propose a semi-supervised defect detection method with improvement of Soft-teacher. Firstly, using mixed data enhancement to bring perturbation to the dataset and further utilize unlabeled images to enhance the effect of consistent training; then Swin-Transformer will be used as the backbone network to reduce the information loss caused by pooling, using shifted feature pyramids to do Multi-scale training, it can use the same label to supervise inputs of different sizes to obtain high-quality pseudo-labeling. Experiments on the NEU-DET dataset show that the method achieves good results in semi-supervised strip defect detection, and obtains 68.1% AP50 detection accuracy with 20% of labeled data, which is high enough to meet the needs of preliminary defect detection and labeling.

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Section: Surface Defect Detectionmentioning

confidence: 99%

“…Defect detection is a key step in controlling the quality of steel. Traditional defect detection uses manual inspection, which is time-consuming and labor-intensive, and requires a certain level of skill for workers [3][4][5]. For different steel products, there is a wide variety of surface defects.…”

Section: Introductionmentioning

confidence: 99%

A Semi-Supervised Method for Steel Surface Defect Detection Based on Soft-teacher

2023

AJCIS

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“…Liu et al 8 extracted the features of the main local binary pattern (LBP) of a fabric to detect fabric defects. Zhao et al 9 combined a pyramid histogram of edge orientation gradients with a support vector machine (SVM), and applied it to fabric defect detection. Hamdi et al 10 combined a GLCM and Euclidean distance to achieve the detection of defects after selecting a threshold.…”

mentioning

confidence: 99%

“…Zhao et al. 9 combined a pyramid histogram of edge orientation gradients with a support vector machine (SVM), and applied it to fabric defect detection. Hamdi et al.…”

mentioning

confidence: 99%

Fabric defect detection based on feature fusion of a convolutional neural network and optimized extreme learning machine

Zhou

Deng

Zhu

et al. 2021

Textile Research Journal

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Aiming to accurately detect various defects in the fabric production process, we propose a fabric defect detection algorithm based on the feature fusion of a convolutional neural network (CNN) and optimized extreme learning machine (ELM). Firstly, we use transfer learning to transfer the parameters of the first 13 convolutional layers and first two fully connected layers of a VGG16 network model as pre-trained by ImageNet to the initial model and fine-tune the parameters. Subsequently, the fine-tuned model is used as a feature extractor to extract features of RGB images and their corresponding L-component images. A principal component analysis is used to reduce the dimensionality of the features and fuse the reduced features. The moth flame optimization (MFO) algorithm is used to initialize the optimization variables of a parallel chaotic search (PCS) algorithm, and the PCS algorithm (as optimized by the MFO algorithm) is used to optimize the input weight and bias of the ELM (i.e., the PCS-MFO-ELM (PMELM)). Finally, the PMELM is used to replace the softmax classifier of the CNN to classify and detect fabric defect features. The experimental results show that on the amplified TILDA dataset, the precision, recall, F1-score, and accuracy rates of this algorithm for fabric holes, stains, warp breaks, dragging, and folds in fabric can reach 98.57%, 98.52%, 98.52%, and 98.50%, respectively, that is, higher than those of other algorithms. Through a validity experiment, this method is shown to be suitable for defect detection for unpatterned fabrics, regular patterned fabrics, and irregularly patterned fabrics.

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Fabric defect classification using prototypical network of few-shot learning algorithm

Zhan

Zhou

2022

Computers in Industry

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Fabric defect detection algorithm based on MFS and SVM

Cited by 7 publications

References 10 publications

A Semi-Supervised Method for Steel Surface Defect Detection Based on Soft-teacher

A Semi-Supervised Method for Steel Surface Defect Detection Based on Soft-teacher

Fabric defect detection based on feature fusion of a convolutional neural network and optimized extreme learning machine

Fabric defect classification using prototypical network of few-shot learning algorithm

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