Computer vision for automatic detection and classification of fabric defect employing deep learning algorithm

Purpose The purpose of this paper is to focus on the design and development of computer-aided fabric defect detection and classification employing advanced learning algorithm. Design/methodology/approach To make a fast and effective classification of fabric defect, the authors have considered a char...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:International journal of clothing science and technology Ročník 31; číslo 4; s. 510 - 521
Hlavní autori: Jeyaraj, Pandia Rajan, Samuel Nadar, Edward Rajan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bradford Emerald Publishing Limited 05.08.2019
Emerald Group Publishing Limited
Predmet:
Accuracy
Algorithms
Artificial neural networks
Automation
CAD
Classification
Computer aided design
Computer vision
Computing time
Datasets
Deep learning
Defects
Design defects
Machine learning
Performance evaluation
Performance indices
Scanners
Sensitivity analysis
Standard data
Success
Textiles
Computer vision
Fabric defect detection
Feature extraction
Defect classifications
Deep learning algorithm
ISSN:0955-6222, 1758-5953
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Purpose The purpose of this paper is to focus on the design and development of computer-aided fabric defect detection and classification employing advanced learning algorithm. Design/methodology/approach To make a fast and effective classification of fabric defect, the authors have considered a characteristic of texture, namely its colour. A deep convolutional neural network is formed to learn from the training phase of various defect data sets. In the testing phase, the authors have utilised a learning feature for defect classification. Findings The improvement in the defect classification accuracy has been achieved by employing deep learning algorithm. The authors have tested the defect classification accuracy on six different fabric materials and have obtained an average accuracy of 96.55 per cent with 96.4 per cent sensitivity and 0.94 success rate. Practical implications The authors had evaluated the method by using 20 different data sets collected from different raw fabrics. Also, the authors have tested the algorithm in standard data set provided by Ministry of Textile. In the testing task, the authors have obtained an average accuracy of 94.85 per cent, with six defects being successfully recognised by the proposed algorithm. Originality/value The quantitative value of performance index shows the effectiveness of developed classification algorithm. Moreover, the computational time for different fabric processing was presented to verify the computational range of proposed algorithm with the conventional fabric processing techniques. Hence, this proposed computer vision-based fabric defects detection system is used for an accurate defect detection and computer-aided analysis system.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0955-6222
1758-5953
DOI:10.1108/IJCST-11-2018-0135