scholarly journals A Method for Defect Detection of Yarn-Dyed Fabric Based on Frequency Domain Filtering and Similarity Measurement

2019 ◽  
Vol 19 (3) ◽  
pp. 257-262 ◽  
Author(s):  
Bo Zhang ◽  
Chunming Tang
Keyword(s):  
Frequency Domain ◽  
Defect Detection ◽  
Automatic Detection ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Similarity Measurement ◽  
Measurement Technology ◽  
Fabric Defect ◽  
Dyed Fabric ◽  
Frequency Domain Filtering

Abstract The detection of defects in yarn-dyed fabric is one of the most difficult problems among the present fabric defect detection methods. The difficulty lies in how to properly separate patterns, textures, and defects in the yarn-dyed fabric. In this paper, a novel automatic detection algorithm is presented based on frequency domain filtering and similarity measurement. First, the separation of the pattern and yarn texture structure of the fabric is achieved by frequency domain filtering technology. Subsequently, segmentation of the periodic units of the pattern is achieved by using distance matching function to measure the fabric pattern. Finally, based on the similarity measurement technology, the pattern’s periodic unit is classified, and thus, automatic detection of the defects in the yarn-dyed fabric is accomplished.

scholarly journals Yarn-Dyed Fabric Defect Detection Based On Autocorrelation Function And GLCM

2015 ◽  
Vol 15 (3) ◽  
pp. 226-232 ◽  
Author(s):  
Dandan Zhu ◽  
Ruru Pan ◽  
Weidong Gao ◽  
Jie Zhang
Keyword(s):  
Autocorrelation Function ◽  
Defect Detection ◽  
Threshold Value ◽  
Detection Algorithm ◽  
Template Image ◽  
Fabric Defect ◽  
Detection Window ◽  
Euclidean Distances ◽  
Dyed Fabric ◽  
Occurrence Matrix

Abstract In this study, a new detection algorithm for yarn-dyed fabric defect based on autocorrelation function and grey level co-occurrence matrix (GLCM) is put forward. First, autocorrelation function is used to determine the pattern period of yarn-dyed fabric and according to this, the size of detection window can be obtained. Second, GLCMs are calculated with the specified parameters to characterise the original image. Third, Euclidean distances of GLCMs between being detected images and template image, which is selected from the defect-free fabric, are computed and then the threshold value is given to realise the defect detection. Experimental results show that the algorithm proposed in this study can achieve accurate detection of common defects of yarn-dyed fabric, such as the wrong weft, weft crackiness, stretched warp, oil stain and holes.

scholarly journals EfficientDet for fabric defect detection based on edge computing

2021 ◽  
Vol 16 ◽  
pp. 155892502110083
Author(s):  
Shaojun Song ◽  
Junfeng Jing ◽  
Yanqing Huang ◽  
Mingyang Shi
Keyword(s):  
Cloud Computing ◽  
Defect Detection ◽  
Textile Industry ◽  
Detection Algorithm ◽  
Edge Computing ◽  
Detection Methods ◽  
Inspection System ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Manual Methods

The productivity of textile industry is positively correlated with the efficiency of fabric defect detection. Traditional manual detection methods have gradually been replaced by deep learning algorithms based on cloud computing due to the low accuracy and high cost of manual methods. Nonetheless, these cloud computing-based methods are still suboptimal due to the data transmission latency between the end devices and the cloud. To facilitate defect detection with more efficiency, a low-latency, low power consumption, easy upgrade, and automatical visual inspection system with the help of edge computing are proposed in this work. Firstly, the method uses EfficientDet-D0 as the detection algorithm, integrating the advantages of lightweight and scalable and can suit the resource-constrained edge device. Secondly, we performed data augmentations on five fabric datasets and verified the adaptability of the model in different types of fabrics. Finally, we transplanted the trained model to the edge device NVIDIA Jetson TX2 and optimized the model with TensorRT to make it detection faster. The performance of the proposed method is evaluated in five fabric datasets. The detection speed is up to 22.7 frame per second (FPS) on the edge device Jetson TX2. Compared with the cloud-based method, the response time is reduced by 2.5 times, with the capability of real-time industrial defect detection.

A universal defect detection approach for various types of fabrics based on the Elo-rating algorithm of the integral image

2019 ◽  
Vol 89 (21-22) ◽  
pp. 4766-4793 ◽  
Author(s):  
Xuejuan Kang ◽  
Erhu Zhang
Keyword(s):  
Defect Detection ◽  
Detection Rate ◽  
Threshold Value ◽  
Detection Time ◽  
Integral Image ◽  
High Detection Rate ◽  
Detection Approach ◽  
Fabric Defect ◽  
Novel Method ◽  
Dyed Fabric

In order to overcome the shortcoming that a fabric defect detection method can only fit into a certain type of fabric, this paper presents a novel method by integrating the idea of the integral image into the Elo-rating algorithm (IIER), which can detect the defects of various types of fabric speedily. Firstly, the golden sub-blocks are extracted from defect-free images. The whole images are divided into many sub-blocks, and the integral images of these sub-blocks are obtained. Next, the R sub-blocks are randomly selected from these integral sub-blocks, and each block is assigned an initial Elo point. Afterwards, the R sub-blocks are matched against all sub-blocks and the Elo points are updated after each competition. Finally, regions with bright defects accumulate high Elo points and regions with dark defects accumulate low Elo points. Thus, the threshold value image can be obtained by thresholding the final Elo points, in which white, gray and black regions correspond to bright, dark-defect and defect-free regions, respectively. The performance of the proposed method is evaluated on databases of three categories of fabric, namely raw fabric, yarn-dyed fabric and patterned fabric. The experimental results show that the IIER is a universal algorithm, which has high detection rate for different types of fabrics; in particular, the average correct detection rate can reach 100% for dot-patterned fabric. In addition, the detection time can be significantly reduced comparing with the Elo-rating algorithm (ER). Particularly for star-patterned fabric, the average detection time per image is 24.18 seconds less than the ER.

A fabric defect detection algorithm via context-based local texture saliency analysis

2015 ◽  
Vol 27 (5) ◽  
pp. 738-750 ◽  
Author(s):  
Zhoufeng Liu ◽  
Chunlei Li ◽  
Quanjun Zhao ◽  
Liang Liao ◽  
Yan Dong
Keyword(s):  
Defect Detection ◽  
Texture Features ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Image Texture ◽  
Content Type ◽  
Local Texture ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Saliency Analysis

Purpose – Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm via context-based local texture saliency analysis. Design/methodology/approach – In the proposed algorithm, a target image is first divided into blocks, then the Local Binary Pattern (LBP) technique is used to extract the texture features of blocks. Second, for a given image block, several other blocks are randomly chosen for calculating the LBP contrast between a given block and the randomly chosen blocks. Based on the obtained contrast information, a saliency map is produced. Finally, saliency map is segmented by using an optimal threshold, which is obtained by an iterative approach. Findings – The experimental results show that the proposed algorithm, integrating local texture features and global image texture information, can detect texture defects effectively. Originality/value – In this paper, a novel fabric defect detection algorithm via context-based local texture saliency analysis is proposed.

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