Fabric defect detection plays an important role in automated inspection and quality control in textile manufacturing. As the fabric images have complex and diverse textures and defects, traditional detection methods show a poor adaptability and low detection accuracy. Robust principal component analysis (RPCA) model that can be used to separate the image into object and background have proven applicable in fabric defect detection. However, how to represent texture feature of the fabric image more effectively is still problematic in this kind of method. In addition, the use of the traditional RPCA may result in low accuracy and more noises in sparse part. In this paper, a novel fabric defect detection method based on multilevel deep features fusion and non-convex total variation regularized RPCA (NTV-RPCA) is proposed. Firstly, the image representation ability is well enhanced through multilevel deep features extracted by a convolutional neural network. Then, the non-convex total variation regularized RPCA is proposed in which total variation constraint significantly reduces the noises in sparse part and non-convex solution is more approximate to the authentic one. Next, multilevel saliency maps generated by the sparse matrixes are fused via RPCA to produce a more reliable detection result. Finally, the defect region is located by segmenting the fused saliency map via a threshold segmentation algorithm. Qualitative and quantitative experiments conducted on two public fabric image databases demonstrate that the proposed method improves the adaptability and detection accuracy comparing to the state-of-the-arts.

Steel defect detection is important for industry production as it is tied to the product quality and production efficiency. However, previous steel defect detection methods based on deep convolutional neural networks heavily rely on large‐scale data for training and tend to have poor generalization ability for a novel defect category. In this paper, a novel few‐shot steel defect detection model based on multi‐scale semantic enhancement representation and mask category information mapping is introduced, where only a few annotated samples are acquired for the novel defect category. More concretely, three main components are built: an information‐guidance enhanced multi‐head detector is proposed to improve the representation of information in meta‐feature maps, a mask category representation module is designed to enhance the category feature representation of the mask region in the support set, and a novel multi‐scale category edge loss function is designed to assist the generation of category reweighting vector. Extensive experiments on the North‐east University few‐shot steel defect data set demonstrate that the proposed method significantly outperforms the state‐of‐the‐art methods and verify its effectiveness through ablation studies.

Fabric defect detection plays an irreplaceable role in the quality control of the textile manufacturing industry, but it is still a challenging task due to the diversity and complexity of defects and environmental factors. Visual saliency models imitating the human vision system can quickly determine the defect regions from the complex texture background. However, most visual saliency-based methods still suffer from incomplete predictions owing to the variability of fabric defects and low contrast with the background. In this paper, we develop a context-aware attention cascaded feedback network for fabric defect detection to achieve more accurate predictions, in which a parallel context extractor is designed to characterize the multi-scale contextual information. Moreover, a top-down attention cascaded feedback module was devised adaptively to select the important multi-scale complementary information and then transmit it to an adjacent shallower layer to compensate for the inconsistency of information among layers for accurate location. Finally, a multi-level loss function is applied to guide our model for generating more accurate prediction results via optimizing multiple side-output predictions. Experimental results on the two fabric datasets built under six widely used evaluation metrics demonstrate that our proposed framework outperforms state-of-the-art models remarkably.

Fabric defect detection plays a critical role for measuring quality control in the textile manufacturing industry. Deep learning-based saliency models can quickly spot the most interesting regions that attract human attention from the complex background, which have been successfully applied in fabric defect detection. However, most of the previous methods mainly adopted multi-level feature aggregation yet ignored the complementary relationship among different features, and thus resulted in poor representation capability for the tiny and slender defects. To remedy these issues, we propose a novel saliency-based fabric defect detection network, which can exploit the complementary information between different layers to enhance the representation features ability and discrimination of defects. Specifically, a multi-scale feature aggregation unit (MFAU) is proposed to effectively characterize the multi-scale contextual features. Besides, a feature fusion refinement module (FFR) composed of an attention fusion unit (AFU) and an auxiliary refinement unit (ARU) is designed to exploit complementary important information and further refine the input features for enhancing the discriminative ability of defect features. Finally, a multi-level deep supervision (MDS) is adopted to guide the model to generate more accurate saliency maps. Under different evaluation metrics, our proposed method outperforms most state-of-the-art methods on our developed fabric datasets.

On account of the issue that there is severe noise in the detection of defects by the traditional low-rank decomposition defect detection method, in this paper, we present an efficient fabric defect detection approach which utilizes the sparse and dense decomposition on the base of low-rank representation. Firstly, the fabric image is uniformly segmented into image blocks. Each image block is spanned into a column vector, which is assembled to constitute the fabric image feature matrix. Then, the sparse and dense mixed low-rank decomposition model is constructed with the introduction of the F norm. The presented model is optimized by alternating direction multiplier method (ADMM) and augmented Lagrange multiplier (ALM), and the low rank array, dense matrix and sparse array are obtained. Finally, a thresholding segmentation approach is employed to detect the defect area by partitioning the salience map. Experimental results demonstrate that the proposed method achieves an efficient detection property, and it is superior to the current approaches.

Based on the principle of the quasi-optimal satellite selection algorithm and the fast satellite selection algorithm, this paper gives a combined satellite selection algorithm. The algorithm introduces a new satellite selection strategy by considering different distributions of the similar satellite group. The computation amount of the combined algorithm is lower than quasi-optimal satellite selection algorithm. The experiment results show this algorithm can get a lower GDOP value than quasi-optimal satellite selection algorithm and close to the minimal GDOP.