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Extracellular proteases of haloarchaea are adapted to function at high concentrations of NaCl, and could find useful applications in industrial or biotechnological processes where hypersaline conditions are desired. The diversity of extracellular proteases produced by haloarchaea is largely unknown, although the genomes of many species have been sequenced and are publicly available. In this study, a gene encoding the extracellular protease Hly176B from the haloarchaeon Haloarchaeobius sp. FL176 was cloned and expressed in Escherichia coli. The catalytic triad Asp-His-Ser was confirmed via site-directed mutagenesis, indicating that Hly176B belongs to the class of serine proteases (halolysin). Unlike previously reported extracellular proteases from haloarchaea, Hly176B remained active in almost salt-free solution for a relatively long time. In addition, Hly176B displayed good tolerance to some metal ions, surfactants and organic solvents, and exerts its highest enzyme activities at 40 °C, pH 8.0 and 0.5 M NaCl. Therefore, this study enriches our knowledge of extracellular proteases, and also expands their applications in various industrial uses.

Fine-texture Image Retrieval, a special case in Content Based Image Retrieval, has high potential application value in many fields. However, there are very few researches focus on this special case. Due to the difference between fine-texture images and common images, general retrieval methods for common images are difficult to apply to fine-texture image retrieval. It is also a challenging issue with several obstacles: variety and complexity of appearance, as well as high requirements for retrieval accuracy. To address this issue, this study proposes a novel approach, called deep multi-view hashing (DMVH), to learn enhanced hash codes for efficient fine-texture image retrieval. We propose to use the first few layers of a deep convolutional neural network for fine-texture image presentation. To avoid overfitting, we employ L0Linear layer instead of the commonly used Linear layer in all the fully connected layers. Then we introduce a pairwise quantified similarity computed on the semantic labels. And the learning process of the model is guided by multiple labels of images. This study takes fabric as an example, and builds a dataset called MFT-fabric-v1, to validate the effectiveness of the DMVH method. On this data set, the proposed model achieved a performance with a mAP value of o.8735. The area under the pr curve is larger than other comparison methods. The experimental results demonstrate that the proposed method outperforms the state-of-the-art on the MFT-fabric-v1 dataset.

Under the production mode of small-batch and multi-item, the recognition of yarn-dyed fabric patterns is a crucial task in the textile industry. In this article, an automatic recognition system based on pixel-level features is proposed to recognize the density, the weave pattern, and the color pattern. In this system, the fabric images are captured by a scanner. First, a method based on the Hough transform is used to correct the skew of the yarns, including warp and weft. Second, the yarns and nodes are located in the enhanced images with a brightness-projection method. The density can be calculated by using the results. Then, the type of each node is identified based on the boundary information. We can obtain the weave pattern after knowing the type of each node. Finally, the fuzzy C-means algorithm is used to determine the color of each node, and thus we obtain the color pattern of the yarn-dyed fabric. Experimental results demonstrate that the proposed recognition system is effective for detecting the structural parameters of yarn-dyed fabric.

Fabric surface flaw inspection is essential for textile quality control, and it is demanding to replace human inspectors with the automatic machine vision-based flaw inspection system. To alleviate the time-consuming problem of sparse coding in detecting phase, this work presents a real time fabric flaw inspection method by using grouped sparse dictionary. Firstly, the over-complete sparse dictionary is learned from normal fabric images; Secondly, the learned sparse dictionary is grouped into several sub-dictionaries by evaluating reconstruction error. Finally, the grouped dictionary is used to represent image and identify flaw regions as they cannot be represented well, leading to large reconstruction error. In addition, a non-maximum suppression algorithm is also proposed to reduce false inspection further. Experiments on various fabric flaws and real-time implementation on the proposed vision-based hardware system are conducted to evaluate the performance of proposed method. In comparison with other dictionary learning methods, the experimental results demonstrate that the proposed method can reduce the running time significantly and achieve a decent performance, which is capable of meeting the real-time inspection requirement without compromising inspection accuracy.

The outbreak of coronavirus has led to an increase in the demand for facemasks globally. Unavailability of appropriate polypropylene non-woven fabrics face masks as a result of inadequate supply to satisfy the growing population has brought about the manufacturing of locally fabrics masks to augment or substitute standard medical class facemasks. The study aims at analyzing airflow of these locally manufactured fabrics to determine possible means of transmitting the virus as well as establish comfort of the user of these masks. Standard polypropylene non-woven, woven and knitted fabrics were considered for the study. Air permeability test was conducted on these fabrics using Frazier Air permeability tester. Depending on the property significant variation in the textile fabrics, polypropylene non-woven is widely accepted for facial masks. Nevertheless, this study illustrates that woven and knitted fabrics have more open structures, which allow a high rate of air penetration and so may require two or three layers to prevent antimicrobial or antiviral potential.

Image processing of digital images is one of the essential categories of image transformation in the theory and practice of digital pattern analysis and computer vision. Automated pattern recognition systems are much needed in the textile industry more importantly when the quality control of products is a significant problem. The printed fabric pattern segmentation procedure is carried out since human interaction proves to be unsatisfactory and costly. Hence, to reduce the cost and wastage of time, automatic segmentation and pattern recognition are required. Several robust and efficient segmentation algorithms are established for pattern recognition. In this paper, different automated methods are presented to segregate printed patterns from textiles fabric. This has become necessary because quality product devoid of any disturbances is the ultimate aim of the textile printing industry.

The recognition of woven fabric pattern is a crucial task for mass manufacturing and quality control in the textile industry. Traditional methods based on image processing have some limitations on accuracy and stability. In this paper, an automatic method is proposed to jointly realize yarn location and weave pattern recognition. First, a new big fabric dataset is established by a portable wireless device. The dataset contains wide kinds of fabrics and detailed fabric structure parameters. Then, a novel multi-task and multi-scale convolutional neural network (MTMSnet) is proposed to predict the location maps of yarns and floats. By adopting the multi-task structure, the MTMSnet can better learn the related features between yarns and floats. Finally, the weave pattern and basic weave repeat are recognized by combining the yarn and float location maps. Extensive experimental results on various kinds of fabrics indicate that the proposed method achieves high accuracy and quality in weave pattern recognition.

In the textile and apparel industry, it remains a challenging task to evaluate the fabric smoothness objectively. In existing studies, the objective fabric smoothness assessment is defined as a typical image classification problem. However, the fabric smoothness labels contain sequence information, and the problem shall be defined as an ordinal classification problem. This article presents an effective method including an image preprocessing algorithm, a compact convolutional neural network(CNN) model, and a label smoothing process. Compared with the commonly used CNN frameworks, the proposed compact CNN model is more suitable for this small-sample and low-abstraction problem. The image processing algorithm can improve the model’s illumination adaptability, and the label smoothing process can modify the model to satisfy the ordinal classification problems better. In the experiments, the method is tested on a fabric image set including 385 graded fabric specimens. Within a 10-fold cross validation, the proposed method achieves 84.00%, 95.38%, and 100% average accuracies under errors of 0 degree, 0.5 degree, and 1 degree respectively. Implementation discussions on preprocessing and label smoothing verify their effectiveness in improving model performance in assessment accuracies and illumination stability. The proposed method outperforms the state-of-the-art methods for fabric smoothness assessment and a series of widely used deep learning methods. Promisingly, the proposed method can provide novel research ideas for the image-based fabric smoothness assessment.

Due to the significant potential values in commercial and social applications, clothing image recognition has recently become a research hotspot, among which clothing attribute recognition is an important content. However, the large variations in the appearance and style of clothing and the image’s complex forming conditions make the task challenging. Moreover, a generic treatment with deep convolutional neural networks cannot provide an ideal solution. Instead of using CNNs for classification, we proposed a novel approach based RCNN framework for the recognition task. Firstly, we apply the modified selective search algorithm to extract the region proposal. Then, the Inception-ResNet V1 model with L-Softmax is employed to represent images and identify their categories. After Soft-NMS, we use a simple neural network to correct the boundary of region box. To evaluate the performance of the framework, a dataset including about 100,000 shirt images was built. The experimental result show that our proposed framework achieved promising overall labeling rate, precision and recall of 87.77%, 73.59% and 83.84%. In addition, comparative experiments demonstrate the superiority of the proposed framework.

In the textile and apparel industry, it remains a challenging task to evaluate the fabric smoothness appearance objectively. In existing studies, with computer vision technology, researchers use the hand-crafted image features and deep convolutional neural network (CNN) based image features to describe the fabric smoothness appearance. This paper presents an image classification framework to evaluate the fabric smoothness appearance degree. The framework contains a feature fusion module to fuse the hand-crafted and CNN features to take both advantages of them. The framework uses the multi-scale spatial masking model and a pre-trained CNN to extract hand-crafted and CNN features of fabric images respectively. In addition, a mislabeled sample filtering module is set in the framework, which helps to avoid the negative impact of mislabeled samples in training. In the experiments, the proposed framework achieves 85.2%, 96.1%, and 100% average evaluation accuracies under errors of 0 degree, 0.5 degree, and 1 degree respectively. The experiments on the feature fusion and mislabeled sample filtering verified their effectiveness in improving the evaluation accuracies and the label noise robustness. The proposed method outperforms the state-of-the-art methods for fabric smoothness assessment. Promisingly, this paper can provide novel research ideas for image-based fabric smoothness assessment and other similar tasks.

This article presents a computer vision method for measuring the geometrical parameters of slub yarn based on yarn sequence images captured from a moving slub yarn. An image segmentation method proposed by our earlier work was applied to segment sequence slub yarn images to obtain overlapping diameter data. Then an image stitching method was proposed to remove the overlapped data based on the normalised cross correlation (NCC) method. In order to detect the geometrical parameters of slub yarn, the frequency histogram , curve fitting, and spectrogram methods were adopted to analyse the sequence diameter data obtained. Four kinds of slub yarn with different geometrical parameters were tested using the method proposed and Uster method. The experimental results show that the detection results for slub amplitude, slub length, slub distance, and slub period obtained using the method proposed were consistent with the set values and Uster results.

Due to the potential values in many areas such as e-commerce and inventory management, fabric image retrieval, which is a special case in Content Based Image Retrieval (CBIR), has recently become a research hotspot. It is also a challenging issue with serval obstacles: variety and complexity of fabric appearance, high requirements for retrieval accuracy. To address this issue, this paper proposes a novel approach for fabric image retrieval based on multi-task learning and deep hashing. According to the cognitive system of fabric, a multi-classification-task learning model with uncertainty loss and constraint is presented to learn fabric image representation. Then we adopt an unsupervised deep network to encode the extracted features into 128-bits hashing codes. Further, the hashing codes are regarded as the index of fabrics image for image retrieval. To evaluate the proposed approach, we expanded and upgraded the dataset WFID, which was built in our previous research specifically for fabric image retrieval. The experimental results show that the proposed approach outperforms the state-of-the-art.

Fabric density measurement plays a key role in the analysis of fabric structural parameters. Existing automatic measurement methods lack varieties of adaptability and present poor performance in practical application. In order to solve these problems, we use convolutional neural networks (CNNs) to locate warps and wefts for woven fabric density measurement. First, we use a portable wireless device to capture high-resolution fabric images and set up a new dataset with labeled yarns location. Based on this dataset, we propose an effective multi-scale convolutional neural network (MSnet) architecture to locate warps and wefts. Then, by using Hough transform and image projection of predicted yarns location, the fabric density is measured accurately. The experimental results emphasize that the proposed method has reached high accuracy under various kinds of patterns and densities of the fabrics and is superior to the state-of-the-art methods in terms of its accuracy and robustness. Promisingly, the proposed method can provide novel ideas for more fabric structural parameter analyses.

For the poor adaptability of the two-dimensional image based fabric smoothness assessment methods for multi-color fabrics, three-dimensional imaging technologies were widely concerned in the area. In this paper, we suggest that the multi-color fabric smoothness assessment can be solved by the two-dimensional image based methods with the help of the proposed multi-color fabric decoloration method. The decoloration problem was solved by a paired image-to-image translation model built by conditional generative adversarial networks that was widely discussed in recent years. To train such model, we proposed a multi-color and white fabric image pairs generation method with a physically based fabric shading model. The experiment results show good performance of the proposed method on both generated image pairs and real multi-color fabric images. To quantitatively evaluate the performance of the decolored fabric images, a set of metrics was established based on the pixel wise image difference, fabric smoothness classification consistency, and manually fabric smoothness evaluation difference. The quantitative evaluation results demonstrated that the proposed method can achieve reasonable results for the multi-color fabric image decoloration.

Fabric smoothness appearance assessment plays an important role in the textile and apparel industry. It remains a challenging task to evaluate the fabric smoothness objectively. In the existing objective methods based on the image analysis, features describing the fabric surface structure were used widely without considering the fact that fabric smoothness is a subjective concept concerned by the human vision perception. This paper proposes an effective method to realize the objective fabric smoothness assessment by analyzing the relationship between the spatial masking effect in the human vision system (HVS) and the fabric smoothness perceived by the human. The spatial masking effect model was improved and extended into the scale space model to imitate the human perception of the fabric smoothness appearance. A set of features was extracted from the fabric images by the model and trained by the support vector machine (SVM) classifier. The method was tested on the fabric image data set, including 385 manually labeled specimens captured by the proposed image acquisition system, and reached 82.60%, 95.84%, and 100% accuracies under the errors of 0°, 0.5°, and 1°, respectively. In the experiments, based on the discussion of the effect of the system parameters, i.e., classification models, training data size, feature scales, and illumination direction number, to the performance of the proposed method, and a set of best system parameters was verified. The final assessment results of the proposed method were illustrated and outperformed the state-of-the-art methods for fabric smoothness assessment and a series of widely used deep learning methods. Promisingly, the proposed method can provide novel research ideas for the image-based fabric smoothness assessment that considering the human perception mechanism.

Fabric image retrieval is a meaningful issue, due to its potential values in many areas such as textile product design, e-commerce, and inventory management. Meanwhile, it is challenging because of the diversity of fabric appearance. Encourage by the recent breakthrough in the deep convolutional neural network (CNN), a deep learning framework is applied for fabric image retrieval. The idea of the proposed framework is that the binary code and feature for representing the image can be learning by a deep CNN when the data labels are available. The proposed framework employs a hierarchical search strategy that includes coarse-level retrieval and fine-level retrieval. Otherwise, a large-scale wool fabric image retrieval dataset named WFID with about 20 000 images are built to validate the proposed framework. The longitudinal comparison experiments for self-parameter optimization and horizontal comparison experiments for verifying the superiority of the algorithm are performed on this data set. The comparison experimental results indicate the superiority of the proposed framework.

The application of image retrieval techniques in industrial settings aims at rapid and accurate retrieval of the same or similar products from an archive to facilitate the production process. Content-based image retrieval and its applications on fabrics focused on the appearance differences and ignored the delicate differences, thus the retrieval fineness cannot meet the demands of industrial applications. In this study, a novel image retrieval strategy was proposed to discriminate appearance differences by the classification model based on transfer learning and realize further discrimination delicate differences by hand-crafted features. The pre-trained model based on ImageNet was fine-tuned to extract features for automatic classification. The images in the wool fabric image database were classified into different categories. Meanwhile, the distribution probabilities were used to build the retrieval strategy. To form the ‘feature database’ in each category, oriented FAST and rotated BRIEF (ORB) was utilized for feature extractions and the vector of locally aggregated descriptors was adopted for feature aggregation. Ball tree was implemented to search the nearest neighbors for the final results. Based on the above methods, the query image was first classified automatically, and then the images were retrieved based on the retrieval strategy. A large-scale image database of wool fabrics with 82073 images was built as the benchmark to evaluate the efficacy of the proposed method. Experiments indicated that the proposed strategy is effective for delicate image retrieval. The combination of transfer learning and hand-crafted features can discriminate the appearance and delicate differences, being superior to the existing methods on representing the delicate fabric differences. The proposed method can provide referential assistance for the production crew and reducing manual labor in the factory.

This paper is the second part of a series reporting the recent development of a computerised method for automatic mosaic sequential yarn images. In our earlier work, an effective method for stitching sequence slub yarn images automatically was developed based on image processing and the normalised cross correlation (NCC) method. 100 image pairs of two kinds of slub yarn were measured in certain specific conditions, such as the frame rate, size of stitching template, etc., and the measurement results were evaluated with the manual method. In this paper, the effects of various influencing factors are numerically examined, including the stitching template size, threshold value, frame rate, and computing time of the mosaic algorithm. The feasibility and accuracy of the fully computerized method were evaluated further under the various influencing parameters. One hundred percent cotton ring spun single slub yarns of 27.8, 15.6, and 9.7 tex were prepared and used for the evaluation. The measurement results obtained by the method proposed are analysed and compared with those measured manually by Adobe Photoshop. The experimental results show that the method proposed can accurately find the stitch position and has a high consistency with the manual method when the matching template is 100 × N pixels, the threshold value T1 ∈ [20, 40] and T2 ∈ [51, 80], and the frame rate is greater than 40 fps.

This paper introduces a new type of moisturizing paper, which makes the material have a stronger moisturizing effect than ordinary transfer paper through the water-based double-layer coating process. At the same time, the paper has a stronger degradation capacity than plastic film paper, more in line with environmental protection requirements. Through the paper printing machine adaptability verification, the paper has easier processing and stronger printing performance. The development and application of this moistening material is a major innovation in the field of moistening packaging materials research.

To realise the density measurement of high-tightness woven fabrics, an efficient inspection method based on the structure relation is developed in this paper. The structure relations of typical HTWF, twill and satin weave are analyzed and a calculation equation of warp density is given with the fabric weave, weft density and wale density. In the experiment, the weft and wale densities are measured with the Fourier transform, image reconstruction and threshold processing based on separately captured images. The warp density is finally calculated based on the mean value of wale and weft density and the given calculation equation constructed with the weave pattern. The experimental results prove that the automatic measurement density system can realize the precise measurement of high-tightness woven fabric density with satisfactory precision and can replace the current manual analysis method.

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.

Yarn density measurement is a significant part of yarn-dyed fabric analysis, traditionally based on reflective image analysis. In this paper, utilizing fabric light transmittance, a method for two-dimensional discrete Fourier transform (2D DFT) analysis on the transmission fabric image is developed for fabric density inspection. First, the power spectrum is generated from the fabric image by a 2D DFT. Next, the yarn skew angles are detected based on the power spectrum analysis. Then the fabric image is reconstructed by an inverse 2D DFT. Finally, projection curves are generated from the reconstructed images and the number of yarns is counted according to the peaks and valleys to obtain the fabric density. Through a comparison between analysis on the reflective and transmission images of multiple-color fabrics, it is proved that the latter method can segment the yarns with more satisfactory accuracy. Furthermore, the experimental and theoretical analyses demonstrate that the proposed method is effective for the density inspection of yarn-dyed fabrics with good robustness and great accuracy.

In this study, a new method for recognizing the parameters of slub-yarn based on image analysis has been proposed. The slub yarn was wrapped on the surface of the black board by YG381 Yarn Evenness Tester. A high resolution scanner was used to acquire the yarn image. Gray stretching and thresholding were carried out to preprocess the image of slub yarn. By separating the slubs from base yarn with different widths, the slub length, slub distance and slub amplitude can be obtained. With the lists of slub length and slub distance, the periodicity rule of slub yarn can be determined. The period of the slubs then will be identified by 1D-Fourier transform. The experiment indicated that the method can identify the parameters of slub yarn with satisfactory results.