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Pattern images can be segmented in a template unit for efficient fabric vision inspection; however, segmentation criteria critically affect the segmentation and defect detection performance. To get the undistorted criteria for rotated images, rotation estimation of absolute angle needs to be proceeded. Given that conventional rotation estimations do not satisfy both rotation errors and computation times, patterned fabric defects are detected using manual visual methods. To solve these problems, this study proposes the application of segmentation reference point candidate (SRPC), generated based on a Euclidean distance map (EDM). SRPC is used to not only extract criteria points but also estimate rotation angle. The rotation angle is predicted using the orientation vector of SRPC instead of all pixels to reduce estimation times. SRPC-based image segmentation increases the robustness against the rotation angle and defects. The separation distance value for SRPC area distinction is calculated automatically. The performance of the proposed method is similar to state-of-the-art rotation estimation methods, with a suitable inspection time in actual operations for patterned fabric. The similarity between the segmented images is better than conventional methods. The proposed method extends the target of vision inspection on plane fabric to checked or striped pattern.

Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.

This paper proposes a robust fabric defect detection method, based on the improved RefineDet. This is done using the strong object localization ability and good generalization of the object detection model. Firstly, the method uses RefineDet as the base model, inheriting the advantages of the two-stage and one-stage detectors and can efficiently and quickly detect defect objects. Secondly, we design an improved head structure based on the Full Convolutional Channel Attention (FCCA) block and the Bottom-up Path Augmentation Transfer Connection Block (BA-TCB), which can improve the defect localization accuracy of the method. Finally, the proposed method applies many general optimization methods, such as attention mechanism, DIoU-NMS, and cosine annealing scheduler, and verifies the effectiveness of these optimization methods in the fabric defect localization task. Experimental results show that the proposed method is suitable for the defect detection of fabric images with unpattern background, regular patterns, and irregular patterns.

Defect detection is very important for guaranteeing the quality and pricing of fabric. A considerable amount of fabric is discarded as waste because of defects, leading to substantial annual losses. While manual inspection has traditionally been the norm for detection, adopting an automatic defect detection scheme based on a deep learning model offers a timely and efficient solution for assessing fabric quality. In real-time manufacturing scenarios, datasets lack high-quality, precisely positioned images. Moreover, both plain and printed fabrics are being manufactured in industries simultaneously; therefore, a single model should be capable of detecting defects in all kinds of fabric. So training a robust deep learning model that detects defects in fabric datasets generated during production with high accuracy and lower computational costs is required. This study uses an indigenous dataset directly sourced from Chenab Textiles, providing authentic and diverse images representative of actual manufacturing conditions. The dataset is used to train a computationally faster but lighter state-of-the-art network, i.e., YOLOv8. For comparison, YOLOv5 and MobileNetV2-SSD FPN-Lite models are also trained on the same dataset. YOLOv8n achieved the highest performance, with a mAP of 84.8%, precision of 0.818, and recall of 0.839 across seven different defect classes.

The traditional manual defect detection method has low efficiency and is time-consuming and laborious. To address this issue, this paper proposed an automatic detection framework for fabric defect detection, which consists of a hardware system and detection algorithm. For the efficient and high-quality acquisition of fabric images, an image acquisition assembly equipped with three sets of lights sources, eight cameras, and a mirror was developed. The image acquisition speed of the developed device is up to 65 m per minute of fabric. This study treats the problem of fabric defect detection as an object detection task in machine vision. Considering the real-time and precision requirements of detection, we improved some components of CenterNet to achieve efficient fabric defect detection, including the introduction of deformable convolution to adapt to different defect shapes and the introduction of i-FPN to adapt to defects of different sizes. Ablation studies demonstrate the effectiveness of our proposed improvements. The comparative experimental results show that our method achieves a satisfactory balance of accuracy and speed, which demonstrate the superiority of the proposed method. The maximum detection speed of the developed system can reach 37.3 m per minute, which can meet the real-time requirements.

On a global scale, the process of automatic defect detection represents a critical stage of quality control in textile industries. In this paper, a semantic segmentation network using a repeated pattern analysis algorithm is proposed for pixel-level detection of fabric defects, which is termed RPDNet (repeated pattern defect network). Specifically, we utilize a repeated pattern detector based on convolutional neural network (CNN) to detect periodic patterns in fabric images. Through the acquired repeated pattern information and proper guidance of the network in a high-level semantic space, the ability to understand periodic feature knowledge and emphasize potential defect areas is realized. Concurrently, we propose a semi-supervised learning scheme to inject the periodic knowledge into the model separately, which enables the model to function independently from further pre-calculation during detection, so there is no additional network capacity required and no loss in detection speed caused. In addition, the model integrates two advanced architectures of DeeplabV3+ and GhostNet to effectively implement lightweight fabric defect detection. The comparative experiments on repeated pattern fabric images highlights the potential of the algorithm to determine competitive detection results without incurring further computational cost.

Online fabric defect detection plays a critical role in the quality management of textile production. However, the high-impact and low-probability characteristics of defective samples lead to redundant design of network and hinder its real-time performance. To improve the time efficiency, this paper proposes a dynamic inference network (DI-Net) which can dynamically allocate computation resources as the complexity of image. Firstly, “AND” Gates are incorporated into the backbone to control activation of network’s function modules, allowing for dynamic adjustment of network depth. Additionally, the dynamic inference module which contains several exits with inference unit is proposed to collaborate with “AND” Gates. When sample’s confidence at specific exit satisfies the early-exit policy, the inference unit will allow it to early-exit from network and output a negative value to corresponding “AND” Gate. As a result, the output of “AND” Gate will also be negative and subsequent network will not be activated. Finally, the two-stage training strategy and exit-weighted loss function are proposed to avoid crosstalk and facilitate different exits to focus on adequate samples, enabling the efficient training of DI-Net. The experiments on the fabric dataset demonstrate that the proposed DI-Net can achieve detection precision and recall over 99% for normal samples, and approximately 95% for defective samples. Besides, its detection speed has been improved by 20%, reaching 30.1 frames per second and 20.96 m/min. This indicates that the proposed DI-Net can meet the requirements of online fabric defect detection.

Currently, fabric defect detection methods predominantly rely on CNN models. However, due to the inherent limitations of CNNs, such models struggle to capture long-distance dependencies in images and fail to accurately detect complex defect features. While Transformers excel at modeling long-range dependencies, their quadratic computational complexity poses significant challenges. To address these issues, we propose combining CNNs with Transformers and introduce Kolmogorov–Arnold Networks (KANs) to enhance feature extraction capabilities. Specifically, we designed a novel network for fabric defect segmentation, named HKAN, consisting of three components: encoder, bottleneck, and decoder. First, we developed a simple yet effective KANConv Block using KAN convolutions. Next, we replaced the MLP in PoolFormer with KAN, creating a lightweight KANTransformer Block. Finally, we unified the KANConv Block and the KANTransformer Block into a Hybrid KAN Block, which serves as both the encoder and bottleneck of HKAN. Extensive experiments on three fabric datasets demonstrate that HKAN outperforms mainstream semantic segmentation models, achieving superior segmentation performance and delivering prominent results across diverse fabric images.