Defect detection in textile fabrics with snake active contour and support vector machines

Bumrungkun, Prachya

doi:10.1088/1742-6596/1195/1/012006

Cited by 6 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An another paper proposes a texture analysis-based approach for fabric defect detection using support vector machines (SVMs) [10]. The method extracts texture features from the fabric image and uses SVMs to detect the defective regions.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Circular Knitting Fabric Defects Using MobileNetV2 Model

HANBAY

2023

Türk Doğa Ve Fen Dergisi

View full text Add to dashboard Cite

Fabric defects cause both labor and raw material losses and energy costs. These undesirable situations negatively affect the competitiveness of companies in the textile sector. Traditionally, human-oriented quality control also has important limitations such as lack of attention and fatigue. Robust and efficient defect detection systems can be developed with image processing and artificial intelligence methods. This study proposes a deep learning-based method to detect and classify common fabric defects in circular knitting fabrics. The proposed method adds a fine-tuned mechanism to the MobileNetV2 deep learning model. The added fine-tuned mechanism is optimized to classify fabric defects. The proposed model has been tested on a fabric dataset containing circular knitting fabric defects. Obtained results showed that the proposed method produced desired results in fabric defect detection and classification.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Classification of Circular Knitting Fabric Defects Using MobileNetV2 Model

HANBAY

2023

Türk Doğa Ve Fen Dergisi

View full text Add to dashboard Cite

show abstract

“…Good performance can be found even with low contrast of images. Bumrungkun et al [ 16 ] proposed a method for edge detection based on snake active contour models, which enables the extraction of fabric feature to detect defects on fabrics and achieves around 98.77% accuracy. Yang et al [ 17 ] proposed an improved algorithm based on an active contour model together with a segmentation algorithm based on an edge-less active contour model.…”

Section: Related Workmentioning

confidence: 99%

A Light-Weight Deep-Learning Model with Multi-Scale Features for Steel Surface Defect Classification

et al. 2020

View full text Add to dashboard Cite

Automatic inspection of surface defects is crucial in industries for real-time applications. Nowadays, computer vision-based approaches have been successfully employed. However, most of the existing works need a large number of training samples to achieve satisfactory classification results, while collecting massive training datasets is labor-intensive and financially costly. Moreover, most of them obtain high accuracy at the expense of high latency, and are thus not suitable for real-time applications. In this work, a novel Concurrent Convolutional Neural Network (ConCNN) with different image scales is proposed, which is light-weighted and easy to deploy for real-time defect classification applications. To evaluate the performance of ConCNN, the NEU-CLS dataset is used in our experiments. Simulation results demonstrate that ConCNN performs better than other state-of-the-art approaches considering accuracy and latency for steel surface defect classification. Specifically, ConCNN achieves as high as 98.89% classification accuracy with only around 5.58 ms latency over low training cost.

show abstract

Yarn Quality Assessment and Monitoring Using ML in Textile Industry

Selva Kumar,

Ramasamy

2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Defect detection in textile fabrics with snake active contour and support vector machines

Cited by 6 publications

References 8 publications

Classification of Circular Knitting Fabric Defects Using MobileNetV2 Model

Classification of Circular Knitting Fabric Defects Using MobileNetV2 Model

A Light-Weight Deep-Learning Model with Multi-Scale Features for Steel Surface Defect Classification

Yarn Quality Assessment and Monitoring Using ML in Textile Industry

Contact Info

Product

Resources

About