Unsupervised fabric defects detection based on spatial domain saliency and features clustering

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.

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.

Fabric defect detection is a crucial aspect of fabric production. At present, deep learning detection methods mostly rely on supervised learning. To tackle this issue, this study proposes an unsupervised fabric defect detection approach based‐on normalising flow. The method only needs to train the mapping of the feature probability distribution of defect‐free samples to a Gaussian distribution. In the inference process, the location of defects can be determined by testing the distance between the probability distribution of image features and the estimated distribution. To adapt to the complex background and various minor defects of the fabric, a feature pyramid structure is adopted. Moreover, considering the gradient vanishing and network degradation caused by deep layers during training, a residual structure is incorporated into the model. Experimental results demonstrate that the feature pyramid flow model outperforms other methods in defect detection across multiple datasets, with an average score rate of 98.7% and 100% for pixel‐level area under the curve (AUC) receiver operating characteristic and image‐level AUC, respectively, compared to an average score rate of 91.1% and 85.4% for other methods.

Aiming at the existing problems of fabric defect detection, the improved YOLOv8 fabric defect detection method SRSE-YOLO is proposed, which effectively improves the model’s ability to extract and utilize complex texture features while maintaining low computational cost, and is more suited to fabric defect detection tasks. To further lighten the model and reduce the computational complexity, this paper introduces SCConv to improve the partial convolution module in the backbone and proposes a new spatial pyramid pooling fusion module RL-SPPF combined with LSKA to simplify the model structure. In the Neck, SCE-C2f combined with an edge detection module is proposed, which addresses for the deficiency of traditional C2f module in dealing with small texture and edge features. In the Detection Head, the EMA attention mechanism is introduced to dynamically adjust the weights of each channel and improve the perception ability of the model to the defect features. Furthermore, Inner-DIoU is used to enhance the loss function, and a 160 × 160 detection head is added, which improves the detection ability of the model for tiny defects and accelerates the convergence of the model. The experimental results show that compared with the benchmark model YOLOv8, the precision ( P ), recall ( R ), mAP@0.5, and mAP@0.5:0.95 indicators of SRSE-YOLO on the Aliyun Tianchi dataset are increased by 5.5%, 4.0%, 3.71%, and 25.68%, respectively. SRSE-YOLO maintains the same level as the benchmark model in terms of parameter count and computational load, realizes the balance between detection performance and calculation resources, effectively improves the detection performance of fabric defects, and provides a new solution for fabric quality control.

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.

Defects in the textile manufacturing process lead to a great waste of resources and further affect the quality of textile products. Automated quality guarantee of textile fabric materials is one of the most important and demanding computer vision tasks in textile smart manufacturing. This survey presents a thorough overview of algorithms for fabric defect detection. First, this review briefly introduces the importance and inevitability of fabric defect detection towards the era of manufacturing of artificial intelligence. Second, defect detection methods are categorized into traditional algorithms and learning-based algorithms, and traditional algorithms are further categorized into statistical, structural, spectral, and model-based algorithms. The learning-based algorithms are further divided into conventional machine learning algorithms and deep learning algorithms which are very popular recently. A systematic literature review on these methods is present. Thirdly, the deployments of fabric defect detection algorithms are discussed in this study. This paper provides a reference for researchers and engineers on fabric defect detection in textile manufacturing.

For rapid detection of defects, this paper selected by the grid method in the whole image defect in the area, and then use the grid template to a defect in the image area can not be narrowed down to so far, and there is only a small area defect image processing. Detection of small defects in the fabric, through the Wavelet transform and other image enhancement comparison and the fabric defect detection experiment found that wavelet transform for image enhancement characteristics of a local image enhancement processing, can achieve better detection of weak targets the effect of small fabric defect detection to improve accuracy and efficiency, real time, to achieve rapid industrial fabric defect detection requirements.

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.

Industrial automatic fabric inspection system, a critical technology in the industry, enhances both total production quantity and quality compared to conventional inspection techniques. This study aims to create a reliable and effective real-time automated visual inspection system for fabrics, focusing on defect detection. The goals of the study can be stated as; installing a system with advanced technology for capturing and processing images swiftly, the development and deployment of a system capable of autonomously learning and scanning fabrics in use, and the creation of a smart framework for accurate fabric defect detection and classification. We focus on the development of unsupervised fabric defect detection using a convolutional autoencoder model, and defect classification using a convolutional neural network model, which takes input as the feature vector generated by the convolutional autoencoder. The experimental outcomes have displayed significant success rates in both detecting defects and classifying them, confirming the effectiveness of the framework in real-time visual inspection systems.

Fabric defect detection (FDD) plays an important role in the quality control in textile industry. In this study, the authors propose an efficient FDD method by using the saliency analysis of multi‐scale local steering kernel (LSK). In the proposed method, a given RGB fabric image is first converted into the Commission International Eclairage (CIE) L*a*b colour space and then the LSK in each colour channel is computed by the singular value decomposition and the centre surrounding definition. Next, the matrix cosine similarity is employed to measure the similarity between different LSK features for generating the desired defective maps. Finally, a multi‐scale averaging fusion scheme is applied to integrate the obtained defective maps at different scales for the final defective map. The experimental results indicate that the proposed method achieves the state‐of‐the‐art performance on FDD compared to the other competitors.

Fabric defect detection is a challenging task in the fabric industry because of the complex shapes and large variety of fabric defects. Many methods have been proposed to solve this problem, but their detection speed and accuracy were very low. As a classic deep learning method and end-to-end target detection algorithm, YOLOv4 has evolved rapidly and has been applied in many industries, showing good performance. This paper proposes an improved YOLOv4 algorithm with higher accuracy for fabric defect detection, in which a new SPP structure that uses SoftPool instead of MaxPool is adopted. The improved YOLOv4 algorithm with three SoftPools can process the feature map effectively, which has a significant advantage in reducing the negative side effects of the SPP structure and improving the detection accuracy. The improved SPP structure is used by the three outputs of Backbone, and in order to ensure that the output can be inputted into the subsequent PANet successfully, the network structure is improved that a series of convolution layers after the SPP structure is added for reducing the channel numbers of feature map to an appropriate value. In addition, contrast-limited adaptive histogram equalization is adopted in advance to improve the image quality, which results in strong anti-interference abilities and can slightly increase the mAP. Experimental results show that, compared with the original YOLOV4, the improved YOLOv4 increases the mAP effectively by 6%, while the FPS only decreases by 2. The improved YOLOv4 can identify the location of defects accurately and quickly, and can also be applied in other defect detection industries.

Detection of fabric defects with intertwined frame vector feature extraction

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.

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.

Fabric defect detection plays an increasingly important role in the industrial automation application for fabric production, but how to detect defects rapidly and accurately is still challenging. In this study, we propose a powerful fabric defect detection method using a hybrid of convolutional neural network (CNN) and variational autoencoder (VAE). The convolutional layers are used for extracting fabric image pattern features and the variational autoencoder is used for modeling the latent characteristics and inferring a reconstruction. The defect positions can be detected by the differences between the original image and the reconstruction image. The proposed method is validated on public patterned fabric datasets. The experimental results demonstrate that the proposed model can achieve outstanding performance in both image level and pixel level defect detection.