Integral images-based approach for fabric defect detection

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.

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.

Template matching has been widely used in image processing for visual inspection of complicated, patterned surfaces. The currently existing methods such as golden template matching and normalized cross correlation are very sensitive to the displacement even the object under test is carefully aligned with respect to the template. This paper proposes a dissimilarity measure based on the optical-flow technique for surface defect detection, and aims at light-emitting diode (LED) wafer die inspection. The dissimilarity measure of each pixel derived from the optical flow field does not represent the true translation distance, but is reliable enough to indicate the degree of difference between an image pair. It is well tolerated to misalignment and random product variation. The integral image technique is applied to replace the sum operations in optical flow computation, and speeds up the intensive computation. We also point out the pitfall of the Lucas-Kanade optical flow when it is applied for defect detection, and propose a swapping process to tackle the problem. The experiment on LED wafer dies has shown that the proposed method can achieve a 100% recognition rate based on a test set of 357 die images.

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.

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.

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.

Aiming at the existing fabric defect detection algorithms with their suboptimal detection accuracy, high model complexity, difficult deployment to the edge of the device, and insufficient ability to meet the problem of real-time detection of fabric defects, we propose a lightweight fabric defect detection model (LWFDD-YOLO) based on the improved YOLOv8n. First, a generalized efficient layer attention network with selective kernel attention (GELAN_SKA) is proposed to replace the C2f module, and selective kernel attention is added to the module to adjust the weights of the convolution kernel according to the different features of different scales, reduce the use of computational resources, thus improving the model’s detection performance and efficiency. Second, a cascaded group attention (CGA) mechanism is added to provide a different input segmentation for each head to enhance the feature diversity of the input attention heads and improve the model’s computational efficiency. An ultra-lightweight dynamic up-sampling operator (Dy_sample) is introduced, which employs a point-sampling-based approach to reduce the consumption of computational resources and improve model performance. Finally, the YOLOv8l framework is utilized to construct a complex model teacher network and the features learned from the teacher network are transferred to the lightweight network proposed in this paper, thus further improving the model performance of the algorithm. The experimental results show that on the self-built fabric defect dataset, the accuracy, recall, and mean average precision ( mAP ) of our algorithm reach 89.4%, 85.2%, and 87.9%, which are 8.9%, 7.2%, and 4.5% higher than the original model, respectively, the number of parameters of the model decreases by 23.4%, the amount of GFLOPs decreases by 25.6%, and the file size is only 9.2 MB. The detection speed can reach 163.4 FPS on GPU. On the AliCloud Tian Chi dataset, the accuracy, recall, and mAP were also improved by 7.4%, 2%, and 4.4%, respectively. The LWFDD-YOLO algorithm proposed in this study can realize real-time detection of fabric defects with relatively obvious improvement in accuracy, and the model requires less memory and is easier to deploy to edge devices, so can be used as a reference for real-time detection of fabric defects.

We investigate the use of Fourier plane nonlinear filtering for phase-encoded images. We investigate the performance of the nonlinear joint transform correlator and the nonlinearly transformed matched filter for phase-encoded images with different types of input noise. We use the peak-to-output-energy ratio, peak-to-sidelobe ratio, and discrimination ratio as the metrics for measuring the performances. We mathematically analyze the peak-to-output-energy ratio of the nonlinearly transformed matched filter for phase-encoded images with spatially nonoverlapping white noise. Computer simulations are provided to show the performance improvements of the nonlinear filtering techniques for the phase-encoded images. In comparison with linear filtering techniques, we find that the nonlinear filtering techniques substantially improve the performance metrics. From the computer-simulation results it can be seen that the nonlinear joint transform correlator performs better than the nonlinearly transformed matched filter in detecting phase-encoded targets in the presence of different types of noise, such as additive overlapping white noise, spatially nonoverlapping white background noise, spatially nonoverlapping colored background noise, and nontarget objects.

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.

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

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.

In the textile manufacturing industry, fabric defect detection becomes a necessary and essential step in quality control. The investment in this field is more than economical when reduction in labor cost and associated benefits are considered. Moreover, the development of a wholly automated inspection system requires efficient and robust algorithms. To overcome this problem, in this paper, we present a new fabric defect detection scheme which uses the local homogeneity and neural network. Its first step consists in computing a new homogeneity image denoted as H-image. The second step is devoted to the application of the discrete cosine transform (DCT) to the H-image and the extraction of different representative energy features of each DCT block. These energy features are used by the back-propagation neural network to judge the existence of fabric defect. Simulations on different fabric images and different defect aspects show that the proposed method achieves an average accuracy of 97.35%.

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.

Detection of fabric defects with intertwined frame vector feature extraction