Fabric quality plays a crucial role in modern textile industry processes. How to detect fabric defects quickly and effectively has become the main research goal of researchers. The You Only Look Once (YOLO) series of networks have maintained a dominant position in the field of target detection. However, detecting small-scale objects, such as tiny targets in fabric defects, is still a very challenging task for the YOLOv4 network. To address this challenge, this paper proposed an improved YOLOv4 target detection algorithm: using a combined data augmentation method to expand the dataset and improve the robustness of the algorithm, obtaining the anchors suitable for fabric defect detection by using the k-means algorithm to cluster the ground truth box of the dataset, adding a new prediction layer in yolo_head in order to have a better effect on tiny target detection, integrating a convolutional block attention module into the backbone feature extraction network, and innovatively replacing the CIOU loss function with the CEIOU loss function to achieve accurate classification and localization of defects. Experimental results show that compared with the original YOLOv4 algorithm, the detection accuracy of the improved YOLOv4 algorithm for tiny targets has been greatly increased, the AP value of tiny target detection has increased by 12%, and the overall mean average precision (mAP) has increased by 3%. The prediction results of the proposed algorithm can provide enterprises with more accurate defect positioning, reduce the defect rate of fabric products, and improve their economic effect. 

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Research on Tiny Target Detection Technology of Fabric Defects Based on Improved YOLO

: Fabric quality plays a crucial role in modern textile industry processes. How to detect fabric defects quickly and effectively has become the main research goal of researchers. The You Only Look Once (YOLO) series of networks have maintained a dominant position in the ﬁeld of target detection. However, detecting small-scale objects, such as tiny targets in fabric defects, is still a very challenging task for the YOLOv4 network. To address this challenge, this paper proposed an improved YOLOv4 target detection algorithm: using a combined data augmentation method to expand the dataset and improve the robustness of the algorithm, obtaining the anchors suitable for fabric defect detection by using the k-means algorithm to cluster the ground truth box of the dataset, adding a new prediction layer in yolo_head in order to have a better effect on tiny target detection, integrating a convolutional block attention module into the backbone feature extraction network, and innovatively replacing the CIOU loss function with the CEIOU loss function to achieve accurate classiﬁcation and localization of defects. Experimental results show that compared with the original YOLOv4 algorithm, the detection accuracy of the improved YOLOv4 algorithm for tiny targets has been greatly increased, the AP value of tiny target detection has increased by 12%, and the overall mean average precision (mAP) has increased by 3%. The prediction results of the proposed algorithm can provide enterprises with more accurate defect positioning, reduce the defect rate of fabric products, and improve their economic effect. the CBL module and upsampling. The fused features will have richer semantic information, and then the feature layer will be downsampled and then passed down and fused with other feature layers. Finally, the feature layer of 104 × 104 will be output to yolo_head for prediction. Strengthening the network’s detection ability for extremely tiny targets, and improving the overall performance of the network for defect detection.

PurposeIn the fabric manufacturing industry, various unfavorable factors, including machine fault and yarn breakage, can easily cause fabric defects and affect product quality, begetting huge economic losses to enterprises. Thus, automatic fabric defect detection systems have become an important development direction. Herein, the most common defects in the fabric production process, like ribbon yarn, broken yarn, cotton ball, holes, yarn shedding and stains, are detected. Current fabric defect detection systems afford low detection accuracy and a high missed detection rate for small target fabric defects. Therefore, this study proposes deep learning technology for automatically detecting fabric defects by improving the YOLOv5s target detection algorithm. The improved algorithm is termed YOLOv5s-4SCK, which can effectively detect fabric defects. This study aims to discuss the aforementioned issues.Design/methodology/approachSpecifically, based on the YOLOv5s algorithm, first, the structure of YOLOv5s is modified to add a small target detection layer, fully utilize deep and shallow features and reduce the missed detection rate of small target fabric defects. Second, the integration of CARAFE upsampling enables the effective retention of feature information and maintenance of a certain computational efficiency, thereby improving the detection accuracy. Finally, the K-Means++ clustering algorithm is used to analyze the position of the center point of the prior box to better obtain the anchor box and improve the average accuracy and evaluation index of detection.FindingsThe research results show that the YOLOv5s-4SCK algorithm increases the accuracy by 4.1% and the detection speed by 2 f.s-1 compared to the original YOLOv5s algorithm, and it effectively improves the original YOLOv5s problem of high missed detection rate of small targets.Research limitations/implicationsThe YOLOv5s-4SCK proposed in this paper can effectively reduce the missed detection rate of fabric defects, improve the detection efficiency and has certain industrial value.Practical implicationsThe proposed algorithm can quickly identify fabric defects, effectively improving the detection rate. In the future, the proposed algorithm will be applied in the actual industry.Social implicationsAutomatic fabric defect detection reduces the manpower of inspectors, and the proposed YOLOv5s-4SCK algorithm is also suitable for other recognition fields.Originality/valueThe proposed YOLOv5s-4SCK algorithm has been tested using real cloth to ensure its accuracy, and its performance is better than the original YOLOv5s algorithm.

Research on improving YOLOv5s algorithm for fabric defect detection

Multi-preference Book Recommendation Method Based on Graph Convolution Neural Network

Highway Accident Localization Based on Virtual Fence for Intelligent Transportation Systems

Fabric defect detection is a key part of product quality assessment in the textile industry. It is important to achieve fast, accurate and efficient detection of fabric defects to improve productivity in the textile industry. For the problems of irregular shapes and many small objects, an improved YOLOv5 object detection algorithm for fabric defects is propose. In order to improve the detection accuracy of small objects, the ASFF(Adaptively Spatial Feature Fusion) feature fusion method is adopted to improve the PANet's bad effect on multi-scale feature fusion. The transformer mechanisms can enhance fused features, allowing the network to focus on useful information. Experimental results show that the mean average precision of the improved YOLOv5 object detection algorithm in fabric defect map detection is 71.70%. The improved algorithm can quickly and accurately improve the accuracy of fabric defect detection and the accuracy of defect localization.

A Fabric Defect Detection System Based Improved YOLOv5 Detector

The technological advantages of 5G networks facilitate the development of location technology for the Internet of Things. The single anchor localization algorithm has been designed using multiple antenna arrays, which enables the accurate and distributed localization of target nodes with a small number of anchor nodes. However, there are privacy breach issues in the localization process. To avoid the privacy breach, in this paper, an efficient privacy-preserving single anchor localization algorithm is proposed by adopting a novel noise-adding mechanism. A set of zero-sum noises has been carefully designed, which not only enables precise localization but also protects location privacy. The performance of the proposed algorithm is analyzed in terms of localization accuracy and privacy preservation. Simulations validate the correctness and theoretical results.

Efficient Privacy Preserving Single Anchor Localization Using Noise-Adding Mechanism for Internet of Things

Graph Convolutional Networks (GCNs) have attracted increasing attention on network representation learning because they can generate node embeddings by aggregating and transforming information within node neighborhoods. However, the classification effect of GCNs is far from optimal on complex relationship graphs. The main reason is that classification rules on relationship graphs rely on node features rather than structure. most of the existing GCNs do not preserve the feature similarity of the node pair, which may lead to the loss of critical information. To address these issues, this paper proposes a node classification network based on semi-supervised learning: Mif-GCN. Specifically, we propose a mixed graph convolution module to adaptively integrate the adjacency matrices of the initial graph and the feature graph in order to explore their hidden information. In addition, we use an attention mechanism to adaptively extract embeddings from the initial graph, feature graph, and mixed graph. The ultimate goal of the model is to extract relevant information for improving classification accuracy. We validate the effectiveness of Mif-GCN on multiple datasets, including paper citation networks and relational networks. The experimental results outperform the existing methods, which further explore the classification rules.

Mixed Multi-channel Graph Convolution Network on Complex Relation Graph.

Fabric defect detection is a key part of product quality assessment in the textile industry. It is important to achieve fast, accurate and efficient detection of fabric defects to improve productivity in the textile industry. For the problems of varying scales, irregular shapes and many small objects, an improved YOLOv4 object detection algorithm for fabric defects is proposed. Firstly, in order to improve the detection accuracy of small objects, the RFB module is introduced and fused with shallow features, which can obtain receptive fields of different scales to improve the features extracted from the backbone network. Secondly, the introduction of spatial and channel attention mechanisms can enhance fused features, allowing the network to focus on useful information. Experimental results show that the mean average precision of the improved YOLOv4 object detection algorithm in fabric defect map detection is 71.89%. The improved algorithm can accurately improve the accuracy of fabric defect positioning.

Fang Zuo

Papers

A Fabric Defect Detection System Based Improved YOLOv5 Detector

Efficient Privacy Preserving Single Anchor Localization Using Noise-Adding Mechanism for Internet of Things

Mixed Multi-channel Graph Convolution Network on Complex Relation Graph.

Fabric Defect Target Detection Algorithm Based on YOLOv4 Improvement