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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.

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.

To improve the detection rate of defect and the fabric product quality, a higher real-time performance fabric defect detection method based on the improved YOLOv3 model is proposed. There are two key steps: first, on the basis of YOLOv3, the dimension clustering of target frames is carried out by combining the fabric defect size and k-means algorithm to determine the number and size of prior frames. Second, the low-level features are combined with the high-level information, and the YOLO detection layer is added on to the feature maps of different sizes, so that it can be better applied to the defect detection of the gray cloth and the lattice fabric. The error detection rate of the improved network model is less than 5% for both gray cloth and checked cloth. Experimental results show that the proposed method can detect and mark fabric defects more effectively than YOLOv3, and effectively reduce the error detection rate.

Fabric defect detection is an important part of the textile industry, aiming at the problems of many types of fabric defects, small size defects and unbalanced samples, an improved YOLOv5 fabric defect detection algorithm, FD-YOLOv5, was proposed. First, the coordinate attention module is embedded in the YOLOv5 backbone network structure to replace the bottleneck structure in the original network model. While reducing the amount of parameters and calculation, it enhances the ability of the network to extract features and improves the model's ability to detect small target defects. Secondly, a smoother Mish activation function is used in the original model convolution structure for model training, which improves the nonlinear expression ability of the model; the SIoU loss function considering the direction of the anchor box is used to improve the convergence speed and detection accuracy of the model. Finally, combining the focal loss and GHM loss functions as the target confidence loss function to solve the problem of sample imbalance in the fabric defect dataset. The experimental results based on the public fabric defect dataset of Aliyun TianChi shows that the mAP@.5 and mAP@.5:.95 of the improved algorithm are 65.1% and 30.4%, respectively, which are 8.3% and 3.2% higher than the original model, respectively, and the parameter amount, calculation amount and weight of the model are reduced by 8.4%, 11.2% and 14.3%, respectively, compared with the original model. Even compared with the state-of-the-art YOLOv7 model, the mAP@.5 value of the proposed model is improved by 6.5%. Although the FPS value is lower than YOLOv7 model, it also achieves a detection speed of 79 frames per second, which can meet the real-time demand. The experimental results demonstrate the effectiveness of the method in this paper, which can provide a reference for the automatic detection method of fabric defects.

In contrast to the common small target detection problems, it is more difficult to locate and identify the small surface defects of fabric due to its own texture and complex background interference. Therefore, this paper proposes an effective detector for small-scale block defects on fabric surface by taking advantage of the backbone which integrates the Coordinate Attention module to enhance the acquisition of small-scale block defect location information. The FPN + PAN multi-scale detection structure is adopted to effectively integrate the feature information between different levels and deal with the multi-scale problem of defects. In the Neck section, a small target detection layer is set to expand the receptive field to prevent the loss of small-scale defect feature information. Moreover, we propose to use the GhostBottleneck module instead of the ordinary downsampling process to eliminate redundant convolutional calculations to improve the detection speed. The experimental results show that the optimal detection results of 0.56 and 0.842 are achieved in the detection recall and accuracy of the public fabric dataset; compared with other detectors, the result of small-scale defect detection rate is reduced by at least 2.7%, and the detection process meets the real-time requirement of automatic defect detection, which verifies the effectiveness of our method. Code and data are available at: https://github.com/VIMLab-hfut/SCG-NET .