Complex Pattern Jacquard Fabrics Defect Detection Using Convolutional Neural Networks and Multispectral Imaging

Abstract

Manual inspection of textiles is a long, tedious, and costly method. Technology has solved this problem by developing automatic systems for textile inspection. However, Jacquard fabrics present a challenge because patterns can be complex and seemingly random to systems. Only a few in-depth studies have been conducted on jacquard fabrics despite their important and intriguing nature. Previous studies on jacquard fabrics are of simple patterns. This paper introduces a new and novel field in fabrics defect detection. Complex-patterned jacquard fabrics are much more challenging. In this paper, novel defect detection models for jacquard-patterned fabrics are presented. Owing to the lack of available databases for jacquard fabrics, we compiled and experimented on our own novel dataset. Our dataset was collected from plain, undyed jacquard fabrics with different complex patterns. In this study, we used and tested several deep learning models with image pre-processing and convolutional neural networks (CNNs) for unsupervised detection of defects. We also used multispectral imaging, combining normal (RGB) and near-infrared (NIR) imaging to improve our system and increase its accuracy. We propose two systems: a semi-manual system using a simple CNN network for operation on separate patterns and an integrated automated system that uses state-of-theart CNN architectures to run on the entire dataset without prior pattern specification. The images are preprocessed using contrast-limited adaptive Histogram Equalization (CLAHE) to enhance their features. We concluded that deep learning is efficient and can be used for defect detection in complex patterns. Proposed method of EfficientNet CNN gave high accuracy reaching 99% approximately. We also found that multispectral imaging is more advantageous and yields higher accuracy.

Fabric Defect Detection Based on Machine Learning

“…Furthermore, there was no need to calculate size of periodicpattern during detection for periodic textured fabric. A defect detection models were presented [12] for the jacquardpatterned fabrics. A dataset was collected from plain, undyed jacquard fabrics with different complex patterns.…”

Deep learning-based image defect detection and removal in manufacturing

“…However, this method is focused on colored jacquard fabric with a square grid without patterns. In [23], the authors proposed a method for detecting defects in jacquard material using convolutional neural networks (CNN).…”

Tomato Quality Classification Based on Transfer Learning Feature Extraction and Machine Learning Algorithm Classifiers

“…Machine learning has found extensive effectiveness in numerous applications, ranging from quality assessment, detection of diseases in crops, the precise recognition of handwritten digits to facilitate natural language processing, and the accurate identification of audio and speech patterns. [6]- [10]. Recently, there has been a significant use of machine learning in the food industry, for sorting and grading vegetables or fruits to tackle the challenges of human errors, subjectiveness, labor costs, time-consuming, and increased performance [11]- [13].…”