Fabric Defect Detection Using Local Homogeneity Analysis and Neural Network

As the textile industry shifts towards smart manufacturing, the detection of fabric defects has emerged as a critical element in ensuring product quality, enhancing productivity, and reducing costs. Computer vision and machine learning techniques, particularly deep learning, have emerged as essential tools in modern textile defect detection. This article provides a review of previous research in this field, examining the technological development and future directions for fabric surface defect detection. The article begins by discussing the various stages of fabric defect detection, which are categorized into four phases: image acquisition, image preprocessing, feature extraction and selection, and classification and detection. It also explores the application of image processing technologies at each stage, offering a detailed analysis of the advantages and limitations of each method through a comprehensive comparison. In addition, this article organizes existing fabric defect datasets and discusses the importance and challenges of datasets in model training. Finally, the article discusses the key challenges in fabric defect detection and outlines potential research directions for the future. The goal of this article is to guide the textile industry in selecting and applying machine vision technology, and to support the development of intelligent fabric inspection systems.

As an essential step in quality control, fabric defect detection plays an important role in the textile manufacturing industry. The traditional manual detection method is inaccurate and incurs a high cost; as a result, it is gradually being replaced by deep learning algorithms based on cloud computing. However, a high data transmission latency between end devices and the cloud has a significant impact on textile production efficiency. In contrast, edge computing, which provides services near end devices by deploying network, computing and storage facilities at the edge of the Internet, can effectively solve the above-mentioned problem. In this article, we propose a deep-learning-based fabric defect detection method for edge computing scenarios. First, this article modifies the structure of DenseNet to better suit a resource-constrained edge computing scenario. To better assess the proposed model, an optimized cross-entropy loss function is also formulated. Afterward, six feasible expansion schemes are utilized to enhance the data set according to the characteristics of various defects in fabric samples. To balance the distribution of samples, proportions of various defect types are used to determine the number of enhancements. Finally, a fabric defect detection system is established to test the performance of the optimized model used on edge devices in a real-world textile industry scenario. Experimental results demonstrate that compared with the conventional convolutional neural network (CNN), the proposed optimized model attains an average improvement of 18% in the area under the curve (AUC) metric for 11 defects. Data transmission is reduced by approximately 50% and latency is reduced by 32% in the Cambricon 1H8 platform compared with a cloud platform.

Purpose – Fabric defects detection is vital in the automation of textile industry. The purpose of this paper is to develop and implement a new fabric defects detection method based on adaptive wavelet. Design/methodology/approach – Fabric defects can be regarded as the abrupt features of textile images with uniform background textures. Wavelets have compact support and can represent these textures. When there is an abrupt feature existed, the response is totally different with the response of the background textures, so wavelets can detect these abrupt features. This method designs the appropriate wavelet bases for different fabric images adaptively. The defects can be detected accurately. Findings – The proposed method achieves accurate detection of fabric defects. The experimental results suggest that the approach is effective. Originality/value – This paper develops an appropriate method to design wavelet filter coefficients for detecting fabric defects, which is called adaptive wavelet. And it is helpful to realize the automation of textile industry.

PurposeIn the fabric manufacturing industry, various unfavorable factors, including machine fault and yarn breakage, can easily cause fabric defects and affect product quality, begetting huge economic losses to enterprises. Thus, automatic fabric defect detection systems have become an important development direction. Herein, the most common defects in the fabric production process, like ribbon yarn, broken yarn, cotton ball, holes, yarn shedding and stains, are detected. Current fabric defect detection systems afford low detection accuracy and a high missed detection rate for small target fabric defects. Therefore, this study proposes deep learning technology for automatically detecting fabric defects by improving the YOLOv5s target detection algorithm. The improved algorithm is termed YOLOv5s-4SCK, which can effectively detect fabric defects. This study aims to discuss the aforementioned issues.Design/methodology/approachSpecifically, based on the YOLOv5s algorithm, first, the structure of YOLOv5s is modified to add a small target detection layer, fully utilize deep and shallow features and reduce the missed detection rate of small target fabric defects. Second, the integration of CARAFE upsampling enables the effective retention of feature information and maintenance of a certain computational efficiency, thereby improving the detection accuracy. Finally, the K-Means++ clustering algorithm is used to analyze the position of the center point of the prior box to better obtain the anchor box and improve the average accuracy and evaluation index of detection.FindingsThe research results show that the YOLOv5s-4SCK algorithm increases the accuracy by 4.1% and the detection speed by 2 f.s-1 compared to the original YOLOv5s algorithm, and it effectively improves the original YOLOv5s problem of high missed detection rate of small targets.Research limitations/implicationsThe YOLOv5s-4SCK proposed in this paper can effectively reduce the missed detection rate of fabric defects, improve the detection efficiency and has certain industrial value.Practical implicationsThe proposed algorithm can quickly identify fabric defects, effectively improving the detection rate. In the future, the proposed algorithm will be applied in the actual industry.Social implicationsAutomatic fabric defect detection reduces the manpower of inspectors, and the proposed YOLOv5s-4SCK algorithm is also suitable for other recognition fields.Originality/valueThe proposed YOLOv5s-4SCK algorithm has been tested using real cloth to ensure its accuracy, and its performance is better than the original YOLOv5s algorithm.

Fabric surface defects can significantly affect the appearance and performance of textile products, often resulting in reduced product quality and economic loss. As a key step in non-destructive quality control, fabric defect detection requires both high accuracy and real-time performance under complex industrial environments. In this study, we present a visual inspection framework based on an improved YOLOv11 architecture for the non-destructive detection of fabric surface defects. A custom image acquisition system, integrating a high-resolution industrial camera and ring-shaped LED lighting, is designed to enhance defect edge contrast and provide high-quality input data. The proposed network incorporates a C3k2-DSConv module with dynamic snake convolution to better capture irregular defect geometries at multiple scales, and introduces a lightweight Slim-Neck structure with GSConv and VoV-GSCSP for efficient feature fusion. Additionally, the WIoU loss function is adopted to stabilise training and improve localisation accuracy. Experimental results on the Cloth_dataset demonstrate a mAP of 96.1%, outperforming existing detectors in both speed and accuracy. This approach provides a practical and efficient solution for real-time, non-destructive defect detection in textile manufacturing lines.

The Fabric Defect (FD) classification system lowers the expenses related to low-quality products by improving productivity and product quality. There are quite a number of existing fabric defect classification techniques, but most of them are not suitable for classification in a complex environment due to insufficient deep feature extraction. Fabric defect detection is difficult in the fabric industry due to complex shapes and a variety of defects. In this research, a novel clustering-based segmentation technique is proposed to segment fabric defects. Initially, the input fabric images are denoised utilizing the Wiener filter to reduce noise artifacts. Using the pre-processed images, the defective and normal regions are segmented using Fuzzy k-means clustering. The defective regions are identified and analyzed, while the normal regions are further classified using a Random Forest classifier to detect minute defects more accurately. Finally, both defective and normal fabric images are concatenated into the Fuzzy U-Net for fabric region segmentation. The proposed Fuzzy U-Net model attains a total accuracy (ACY) of 8.87%, 8.34%, 2.85%, and 4.51% better than DeepLabV3, seg net, Mask RCNN, and U-net, respectively. The proposed Clustering-based segmentation model enhances the total ACY by 30.14%, 14.65%, 55.16%, and 11.98% better than CNN, MSCDAE, FabricNet, and FN-Net, respectively. The proposed clustering-based segmentation technique yields higher accuracy than the existing models. Compared to CNNs and other deep learning models, the clustering-based segmentation significantly improves accuracy. Real-time fabric inspection is possible with it since it allows accurate defect detection in complex environments.

Fabric defect detection is of great importance in the modern textile industry. In actual production, subjective factors usually affect manual detection, which is prone to problems such as false and missed detection. With the development of computer vision, a large number of fabric defect automatic detection algorithms have been proposed. Traditional algorithms rely too heavily on setting parameters manually, while deep learning algorithms have expensive training and computing costs. Given these limitations, a new fabric defect detection method based on the latest cartoon texture image decomposition model, visual saliency algorithm, and mathematical morphology, is proposed in this article. A digital image acquisition system is also designed and constructed. Therefore, the self-made dataset used in the experiment is composed of self-collected images and network public images. To further evaluate the performance of the proposed method, this study conducted fabric defect detection experiments and comparison experiments based on the self-made dataset. The results show that this method can successfully detect fabric defects, having high detection accuracy and efficiency. Combining subjective vision and objective evaluation, comparison experiments prove that this method is superior to other common fabric defect detection methods and has the highest value of accuracy and F1-score. This research provides a new method and technical support for fabric defect detection and other fields.

Fabric defect detection is an important process in quality assurance of textile industry. With the development of computer vision technology, using digital image processing to detect fabric defects has become a focus of image application research. For the low efficiency of manual fabric detecting and the problem of high rate and the miss rate, combined with the uniform fabric texture and the variety and small scale of fabric defect data, the paper puts forward a monochromatic fabric flaw detection of convolution neural network (CNN) algorithm, designs the combination image enhancement technique of shallow convolution neural network structure. The collected fabric image is enhanced by image histogram equalization, noise reduction and edge detection, highlighted the texture characteristics and drawbacks of fabric area, the algorithm improves the efficiency of convolutional neural network to extract effective features of fabric, reduces unnecessary feature extraction, and effectively reduces the error rate caused by overfitting of neural network model. Experimental results show that the algorithm can achieve high accuracy, reaching 94.1% in PC GPU mode.

To detect defects in fabrics more efficiently, easily and accurately, a method based on Local Binary Pattern (LBP) is proposed in this paper. The main purpose of this algorithm is to extract the feature value of fabric images. Firstly the feature of the whole defect-free fabric image is got with LBP algorithm. Then the image is divided into small detection windows, and the feature of each window can be obtained. Compare their similarity calculated by Chi-square function to get the threshold. Then process the defective images according to the same procedure. At last compare the similarity with the threshold to obtain defect regions. The defects are detected at the same time. Experimental results demonstrate that, LBP algorithm is effective in the area of detecting defects of fabrics.

Generic object detection algorithms for natural images have been proven to have excellent performance. In this paper, fabric defect detection on optical image datasets is systematically studied. In contrast to generic datasets, defect images are multi-scale, noise-filled, and blurred. Back-light intensity would also be sensitive for visual perception. Large-scale fabric defect datasets are collected, selected, and employed to fulfill the requirements of detection in industrial practice in order to address these imbalanced issues. An improved two-stage defect detector is constructed for achieving better generalization. Stacked feature pyramid networks are set up to aggregate cross-scale defect patterns on interpolating mixed depth-wise block in stage one. By sharing feature maps, center-ness and shape branches merges cascaded modules with deformable convolution to filter and refine the proposed guided anchors. After balanced sampling, the proposals are down-sampled by position-sensitive pooling for region of interest, in order to characterize interactions among fabric defect images in stage two. The experiments show that the end-to-end architecture improves the occluded defect performance of region-based object detectors as compared with the current detectors.

Due to the complexity of the texture background and the diversity of the defect types of the fabric image, the traditional method of fabric defect detection shows poor detection performance. Recent advances on salient object detection benefit from Fully Convolutional Neural Network (FCN) and achieve the good performance. Meanwhile, the deficiencies in a fabric image are salient compared to the texture background, so the saliency model is very feasible for fabric defect detection. In this paper, we propose a novel saliency model based on discriminative feature and bi-directional message interaction for fabric defect detection. Firstly, we design a multi-scale attention-guided feature extraction module in which the multi-scale context-aware feature extraction block and channel attention block are respectively used to capture multi-scale contextual information and assign greater weight to more discriminative features corresponding to the right fabric defect scale. Then a bi-directional message interaction module is designed to promote the effectiveness of feature with specific resolution by interacting message along both directions, which further improves the availability of the feature extraction. After the bi-directional message interaction module, we use a cross-level contrast feature extraction module, which elevates features with locally strong contrast along each resolution axis, to predict saliency maps. Finally, the predicted saliency maps are efficiently merged to produce the final prediction result. We conduct extensive experiments to evaluate our net and experiment results demonstrate that the proposed method outperforms the state-of-the-art approaches.KeywordsFabric defect detectionSaliency modelDiscriminative featureFCN

This paper presents a novel approach to the fast detection and extraction of fabric defects from the images of textile fabric. Automated visual inspection systems are much needed in the textile industry, especially when the quality control of products in textile industry is a significant problem. In the manual fault detection systems with trained inspectors, very less percentage of the defects are being detected while a real time automatic system can increase this to a maximum number. Thus, automated visual inspection systems play a great role in assessing the quality of textile fabrics. For the detection of fabric defects, we first decompose the image into its bit planes. The lower order bit planes are found to carry important information of the location and shape of defects. Then we find the exact location by means of mathematical morphology. The algorithm has been tested on a subset of TILDA1 image database with various visual qualities. Robustness with respect to the changes of the parameters of the algorithm has been evaluated.

<p>In the field of fabric manufacturing, many factories still utilise the traditional manual detection method. It requires a lot of labour, resulting in high error rates and low efficiency. In this paper, we represent a realtime automated detection method based on object as point. This work makes three attributions. First, we build a fabric defects database and augment the data to training the intelligence model. Second, we provide a real-time fabric defects detection algorithm, which have potential to be applied in manufacturing. Third, we figure out CenterNet with soft NMS will improved the performance in fabric defect detection area, which is considered an NMS-free algorithm. Experiment results indicated that our lightweight network based method can effectively and efficiently detect five different fabric defects.</p>

Automatic detection of textile defects is a crucial factor in improving textile quality. Fast and accurate detection of these defects is key to achieving automation in the textile industry. However, the detection of textile defects faces challenges such as small defect targets, low contrast between defects and the background, and significant variations in the aspect ratio of defects. To address these issues, this study proposes a new method for textile defect detection based on an improved version of You Only Look Once Version 8(YOLOv8) called DA-YOLOv8s. Deep & Cross Network(DCNv2) is introduced into the Backbone Network to replace the C2F module, enhancing the extraction of network features; an self-attention mechanism, Polarized Self-Attention(PSA), is adopted to increase feature fusion capability and reduce feature loss in both channel and spatial dimensions; finally, a Small Object Detection Head (SOHead) is added to improve the feature extraction ability for small targets. Experimental results show that the improved YOLOv8 algorithm achieves has achieved mAP@0.5 and mAP of 44.6% and 48.6% respectively, which is an improvement of 4.2% and 3.8% over the original algorithm, and also outperforms the Optimal YOLOv9s model and the latest YOLOv11s model in these two metrics. The speed of textile defect detection has reached 257.38 frames per second (FPS) and the floating-point operation speed is 36.6 GFLOPS, ensuring the accuracy and speed of textile defect detection, with practical engineering application value.

We propose a new method for fabric defect detection by incorporating the design of an adaptive wavelet-based feature extractor with the design of an Euclidean distance-based detector. The proposed method characterizes the fabric image with multiscale wavelet features by using undecimated discrete wavelet transforms. Each nonoverlapping window of the fabric image is then detected as defect or nondefect with an Euclidean distance-based detector. Instead of using the standard wavelet bases, an adaptive wavelet basis is designed for the detection of fabric defects. Minimization of the detection error Is achieved by incorporating the design of the adaptive wavelet with the design of the detector parameters using a discriminative feature extraction (DFE) training method. The proposed method has been evaluated on 480 defect samples from five types of defects, and 480 nondefect samples, where a 97.5% detection rate and 0.63% false alarm rate were achieved. The evaluations were also carried out on unknown types of defects, where a 93.3% detection rate and 3.97% false alarm rate were achieved in the detection of 180 defect samples and 780 nondefect samples. © 2002 Society of Photo-Optical Instrumentation Engineers.