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The purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in textile manufacturing.

This paper proposed a fabric defect detection algorithm based on cascaded low-rank decomposition. First, the constructed Gabor feature matrix is divided into a low-rank matrix and sparse matrix using low-rank decomposition technique, and the sparse matrix is used as priori matrix where higher values indicate a higher probability of abnormality. Second, we conducted the second low-rank decomposition for the constructed texton feature matrix under the guidance of the priori matrix. Finally, an improved adaptive threshold segmentation algorithm was adopted to segment the saliency map generated by the final sparse matrix to locate the defect regions.

The proposed method was evaluated on the public fabric image databases. By comparing with the ground-truth, the average detection rate of 98.26% was obtained and is superior to the state-of-the-art.

The cascaded low-rank decomposition was first proposed and applied into the fabric defect detection. The quantitative value shows the effectiveness of the detection method. Hence, the proposed method can be used for accurate defect detection and automated analysis system.

The purpose of this paper is to find an efficient fabric defect detection algorithm by means of exploring the sparsity characteristics of main local binary pattern (MLBP) extracted from the original fabric texture.

In the proposed algorithm, original LBP features are extracted from the fabric texture to be detected, and MLBP are selected by occurrence probability. Second, a dictionary is established with MLBP atoms which can sparsely represent all the LBP. Then, the value of the gray-scale difference between gray level of neighborhood pixels and the central pixel, and the mean of the difference which has the same MLBP feature are calculated. And then, the defect-contained image is reconstructed as normal texture image. Finally, the residual is calculated between reconstructed and original images, and a simple threshold segmentation method can divide the residual image, and the defective region is detected.

The experiment result shows that the fabric texture can be more efficiently reconstructed, and the proposed method achieves better defect detection performance. Moreover, it offers empirical insights about how to exploit the sparsity of one certain feature, e.g. LBP.

Because of the selected research approach, the results may lack generalizability in chambray. Therefore, researchers are encouraged to test the proposed propositions further.

In this paper, a novel fabric defect detection method which extracts the sparsity of MLBP features is proposed.

The purpose of this paper is to focus on the design of a dual-branch balance saliency model based on fully convolutional network (FCN) for automatic fabric defect detection, and improve quality control in textile manufacturing.

This paper proposed a dual-branch balance saliency model based on discriminative feature for fabric defect detection. A saliency branch is firstly designed to address the problems of scale variation and contextual information integration, which is realized through the cooperation of a multi-scale discriminative feature extraction module (MDFEM) and a bidirectional stage-wise integration module (BSIM). These modules are respectively adopted to extract multi-scale discriminative context information and enrich the contextual information of features at each stage. In addition, another branch is proposed to balance the network, in which a bootstrap refinement module (BRM) is trained to guide the restoration of feature details.

To evaluate the performance of the proposed network, we conduct extensive experiments, and the experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) approaches on seven evaluation metrics. We also conduct adequate ablation analyses that provide a full understanding of the design principles of the proposed method.

The dual-branch balance saliency model was proposed and applied into the fabric defect detection. The qualitative and quantitative experimental results show the effectiveness of the detection method. Therefore, the proposed method can be used for accurate fabric defect detection and even surface defect detection of other industrial products.

This paper aims to propose a new method to solve the two problems in fabric defect detection. Current state-of-the-art industrial products defect detectors are deep learning-based, which incurs some additional problems: (1) The model is difficult to train due to too few fabric datasets for the difficulty of collecting pictures; (2) The detection accuracy of existing methods is insufficient to implement in the industrial field. This study intends to propose a new method which can be applied to fabric defect detection in the industrial field.

To cope with exist fabric defect detection problems, the article proposes a novel fabric defect detection method based on multi-source feature fusion. In the training process, both layer features and source model information are fused to enhance robustness and accuracy. Additionally, a novel training model called multi-source feature fusion (MSFF) is proposed to tackle the limited samples and demand to obtain fleet and precise quantification automatically.

The paper provides a novel fabric defect detection method, experimental results demonstrate that the proposed method achieves an AP of 93.9 and 98.8% when applied to the TILDA(a public dataset) and ZYFD datasets (a real-shot dataset), respectively, and outperforms 5.9% than fine-tuned SSD (single shot multi-box detector).

Our proposed algorithm can provide a promising tool for fabric defect detection.

The paper includes implications for the development of a powerful brand image, the development of “brand ambassadors” and for managing the balance between stability and change.

This work provides technical support for real-time detection on industrial sites, advances the process of intelligent manual detection of fabric defects and provides a technical reference for object detection on other industrial

Therefore, our proposed algorithm can provide a promising tool for fabric defect detection.

Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm using learned dictionary-based visual saliency.

First, the test fabric image is splitted into image blocks, and the learned dictionary with normal samples and defective sample is constructed by selecting the image block local binary pattern features with highest or lowest similarity comparing with the average feature vector; second, the first L largest correlation coefficients between each test image block and the dictionary are calculated, and other correlation coefficients are set to zeros; third, the sum of the non-zeros coefficients corresponding to defective samples is used to generate saliency map; finally, an improve valley-emphasis method can efficiently segment the defect region.

Experimental results demonstrate that the generated saliency map by the proposed method can efficiently outstand defect region comparing with the state-of-the-art, and segment results can precisely localize defect region.

In this paper, a novel fabric defect detection scheme is proposed via learned dictionary-based visual saliency.

Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm via context-based local texture saliency analysis.

In the proposed algorithm, a target image is first divided into blocks, then the Local Binary Pattern (LBP) technique is used to extract the texture features of blocks. Second, for a given image block, several other blocks are randomly chosen for calculating the LBP contrast between a given block and the randomly chosen blocks. Based on the obtained contrast information, a saliency map is produced. Finally, saliency map is segmented by using an optimal threshold, which is obtained by an iterative approach.

The experimental results show that the proposed algorithm, integrating local texture features and global image texture information, can detect texture defects effectively.

In this paper, a novel fabric defect detection algorithm via context-based local texture saliency analysis is proposed.

The purpose of this paper is to present an automated method for complicated truss structure subassembly identification.

A community-detecting algorithm is introduced and adapted to reach the target. The ratio between oriented bounding boxes of parts is used as the weight to reflect the compact degree of assembly relationships. The authors also propose a method to merge nodes together at cut-vertex in model, by which the solving process could be accelerated.

This method could identify the subassemblies of complex truss structures according to the specific requirements.

This research area is limited to truss structures. This research offers a new method in assembly sequences planning area. It could identify subassemblies in complex truss structures, with which the existing method is not adequate to deal.

This method could facilitate the complex truss structures assembly planning, lower the human errors and reduce the planning time.

The method could inspire general assembly analysis planning.

All authors of this paper confirm that this manuscript is original and has not been submitted or published elsewhere.

Automatic robots can improve the efficiency of liquefied petroleum gas (LPG) tank inspection and maintenance, but it is difficult to achieve high-precision spatial positioning and navigation on tank surfaces. The purpose of this paper is to develop a spatial positioning robotic system for tank inspection. The robot can accurately identify and track weld paths. The positioning system can complete robot’s spatial positioning on tank surfaces.

A tank inspection robot with curvature-adaptive transmission mechanisms is designed in this study. A weld path recognition method based on deep learning is proposed to accurately identify and extract weld paths. Integrated multiple sensors, the positioning system is developed to improve the robot’s spatial positioning accuracy. Experiments are conducted on a cylindrical tank to test weld seam tracking accuracy and spatial positioning performance of the robotic system. The practicality of the robotic system is then verified in field tests.

The robot can accurately identify and track weld seams with a maximum drift angle of 4° and a maximum offset distance of ±30 mm. The positioning system has excellent positioning accuracy and stability. The maximum angle and height errors are 3° and 0.08 m, respectively.

The positioning system can improve the autonomous performance of inspection robots and solve the problems of weld path recognition and spatial positioning. Application of the robotic system can promote the automatic inspection and maintenance of LPG tanks.

The purpose of this paper is to study the transition process from the crystalline particles appearing before the pump inlet to the stable operation of the pump.

Firstly, a modeling test method was put forward for the high-temperature molten salt pump. Then, according to a modeling test scheme, the experiment of the solid–liquid two-phase flow was carried out by using a model pump similar to the prototype pump. Meanwhile, the numerical method to simulate the transition process of a molten salt pump was studied, and the correctness of the numerical model was verified by the experimental results. Finally, the transition process of the molten salt pump was studied by the verified numerical model in detail.

In the simulation of the transition process, it is more accurate to judge the end of the transition process based on the unchanged particle volume fraction (PVF) at the pump outlet than on the periodic fluctuation of the outlet pressure. The outlet pressure is closely related to the PVF in the pump. The variation of the outlet pressure is slightly prior to that of the PVF at the pump outlet and mainly affected by the PVF in the impeller and volute. After 0.63 s, the PVF at each monitoring point changes periodically, and the time-averaged value does not change with time.

This study is of great significance to further improve the design method of molten salt pump and predict the abrasion characteristic of the pump due to interactions with solid particles.

A numerical method is established to simulate the transition process of a molten salt pump, and a method is proposed to verify the numerical model of two-phase flow by modeling test.