Shuai Lu

• PhD
• Beijing Institute of Technology

Yarn-dyed Fabric have a wide variety of patterns. But in the production process, the defective yarn-dyed fabric is often a small amount, so the unsupervised defect detection method of yarn-dyed fabric is increasingly used. In this paper, we proposed an unsupervised defect detection network for yarn-dyed fabrics, which is called Masked Contrastive G...

Recent studies highlighted a practical setting of unsupervised anomaly detection (UAD) that builds a unified model for multi-class images, serving as an alternative to the conventional one-class-one-model setup. Despite various advancements addressing this challenging task, the detection performance under the multi-class setting still lags far behi...

Anomaly detection is an important task for medical image analysis, which can alleviate the reliance of supervised methods on large labelled datasets. Most existing methods use a pixel-wise self-reconstruction framework for anomaly detection. However, there are two challenges of these studies: 1) they tend to overfit learning an identity mapping bet...

Unsupervised anomaly detection (UAD) aims to recognize anomalous images based on the training set that contains only normal images. In medical image analysis, UAD benefits from leveraging the easily obtained normal (healthy) images, avoiding the costly collecting and labeling of anomalous (unhealthy) images. Most advanced UAD methods rely on frozen...

The scarcity of defect samples and the imbalance of defect types lead to the fact that achieving defect detection in color-patterned fabrics remains a challenge in the textile industry. Defect detection methods based on traditional auto-encoder are difficult to solve the problem of defect detection in complex color-patterned fabrics. In order to so...

Aiming at the defects in the process of fabric production, a defect detection model of fabric based on a mixed‐attention‐based multi‐scale non‐skipping U‐shaped deep convolutional autoencoder (MADCAE) was proposed. In traditional encoder, the convolutional layer treats each pixel equally, so the importance of different pixels cannot be reflected.....

Purpose The defect detection problem of color-patterned fabric is still a huge challenge due to the lack of manual defect labeling samples. Recently, many fabric defect detection algorithms based on feature engineering and deep learning have been proposed, but these methods have overdetection or miss-detection problems because they cannot adapt to...

Detecting defects of yarn‐dyed fabrics automatically in industrial scenarios can improve economic efficiency, but the scarcity of defect samples makes the task more challenging in the customised and small‐batch production scenario. At present, most reconstruction‐based methods have high requirements on the effect of reconstructing the defect area i...

Most advanced unsupervised anomaly detection (UAD) methods rely on modeling feature representations of frozen encoder networks pre-trained on large-scale datasets, e.g. ImageNet. However, the features extracted from the encoders that are borrowed from natural image domains coincide little with the features required in the target UAD domain, such as...

Glaucoma is a serious eye disease and glaucoma optic disc hemorrhage (GODH) is an important diagnostic indicator for glaucoma. Deep-learning-based medical image segmentation methods for automatic optic cup and disc segmentation have made tremendous progress. However, when it comes to the segmentation of GODH, classical deep learning technologies fa...

The detection and location of yarn-dyed fabric defects is a crucial and challenging problem in actual production scenarios. Recently, unsupervised fabric defect detection methods based on convolutional neural networks have attracted more attention. However, the convolutional neural networks often neglect to model the global receptive field of image...

Color-patterned fabrics possess changeable patterns, low probability of defective samples, and various forms of defects. Therefore, the unsupervised inspection of color-patterned fabrics has gradually become a research hotspot in the field of fabric defect detection. However, due to the redundant information of skip connections in the network and t...

Glaucoma is a chronic neuro-degenerative condition that is one of the world's * These authors contributed equally to the work.

Defects on the surface of yarn-dyed fabrics are one of the important factors affecting the quality of fabrics. Defect detection is the core link of quality control. Due to the diversity of yarn-dyed fabric patterns and the scarcity of defect samples, reconstruction-based unsupervised deep learning algorithms have received extensive attention in the...

The pattern style of color‐patterned fabrics is varied. Defective fabric samples are scarce in the production of small batch color‐patterned fabrics. Therefore, the unsupervised defect detection method of color‐patterned fabric has attracted wide attention. Many unsupervised defect detection methods for color‐patterned fabrics based on convolutiona...

Automatic color‐patterned fabric defect detection is essential and challenging in controlling manufacturing quality. Due to the scarcity of defect samples of color‐patterned fabric and the imbalance of defect types, an auto‐encoder trained with defect‐free samples has been used. However, the auto‐encoder sometimes has weak generalization ability, l...

This study proposes an unsupervised learning‐based reconstructed scheme and a residual‐analysis‐based defect detection model for color‐patterned fabric defect detection problems in the clothing process industry. It solves the challenging problems of existing supervised fabric defect detection methods, such as high costs in manually labeling samples...

Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both two modalities of images have prominent biomarkers to indicate glaucoma suspected. Clinically, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerou...

The application of deep learning algorithms for medical diagnosis in the real world faces challenges with transparency and interpretability. The labeling of large-scale samples leads to costly investment in developing deep learning algorithms. The application of human prior knowledge is an effective way to solve these problems. Previously, we devel...

Glaucoma is a chronic eye disease that can cause permanent visual loss and is difficult to detect early. Retinal nerve fiber layer defect (RNFLD) is clinical evidence for the diagnosis of glaucoma. Classical deep learning based methods can be used to segment RNFLD from fundus images. However, the segmentation results of these methods do not have th...

The increasing population of orbital debris is considered as a growing threat to space missions. During the recent decades, many enabling space debris capturing and removal methods were investigated. Thus, estimating automated recognition and on-board pose in an uncooperative target spacecraft by implementing using passive sensors such as monocular...

Glaucoma is a serious eye disease that can cause permanent blindness and is difficult to diagnose early. Optic disc (OD) and optic cup (OC) play a pivotal role in the screening of glaucoma. Therefore, accurate segmentation of OD and OC from fundus images is a key task in the automatic screening of glaucoma. In this paper, we designed a U-shaped con...

Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc...

Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc...