Patterned fabric defect detection system using near infrared imaging

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.

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“…8. This approach was inspired by [29]. The imaging system provides proper lighting for both RGB and NIR imaging.…”

Section: A Imaging Systemmentioning

confidence: 99%

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

2022

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“…It can segment small texture accurately, but it is challenging to apply to large texture fabric. Besides, fabric defect detection methods using infrared imaging have also been applied [15]. In summary, traditional image processing methods usually rely on manual setting of parameters, and it is challenging to meet the requirements of real-time detection.…”

Section: Related Workmentioning

confidence: 99%

Pixel-Wise Fabric Defect Detection by CNNs Without Labeled Training Data

Wang

Jing

2020

IEEE Access

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Surface inspection is a necessary process of fabric quality control. However, it remains a challenging task owing to diverse types of defects, various patterns of fabric texture, and application requirements for detection speed. In this paper, a lightweight deep learning model is therefore proposed to complete the segmentation of fabric defects. The input of the model is a fabric image, and the output is a binary image. Generally known, a deep learning model usually needs much data to update the parameters. Still, as an abnormal phenomenon, fabric defects are unpredictable, which makes it impossible to collect a large number of data. Distinct from other models, the proposed method is a supervised network but does not need manually labeled samples for training. A fake sample generator is designed to simulate the defect image, which only needs the defect-free fabric image. The proposed model is trained with fake samples and verified with real samples. The experimental results show that the model trained with false data is useful and achieves high segmentation accuracy on real fabric samples. Besides, a loss function is proposed to deal with the problem of imbalance between the number of background pixels and the number of defective pixels in the fabric image. Comprehensive experiments were performed on representative fabric samples to verify the segmentation accuracy and detection speed of this method. INDEX TERMS Fabric defect; deep learning; image segmentation; defect detection; imbalanced dataset.

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