The defect detection of non-woven fabrics can help enterprises improve production efficiency and save costs. Due to the local characteristics of the convolution kernel, the object detection algorithms based on CNN lack the global modeling of the image, and the detection effect is not ideal for defect detection with a large range of scale changes. Therefore, a non-woven fabric defect detection method is proposed based on the combination of Swin Transformer and YOLOv5, which encodes and decodes features through its powerful self-attention. The network can obtain a larger receptive field and fully relate to the context. The layered construction based on the feature pyramid of Swin coincides with the design of the neck of YOLOv5. It can help the network predict the target on the multi-scale feature map. On this basis, CBAM attention mechanism is introduced to help the network focus on important information. Through Mosaic and MixUp data augmentation, the data distribution is enriched and the robustness is increased. Finally, the anchor size of the prediction target frame is fine-tuned to make the regression prediction more accurate. The effectiveness of the proposed method is verified on the self-made data set, and the detection performance of non-woven fabrics is improved.

Aiming at the problem of insufficient representation of interactive semantic information in the double human interaction behavior recognition method based on graph convolutional neural networks，a new double human interactive spatial?temporal graph convolution network (DHI?STGCN) was proposed for behavior recognition. The network contains spatial sub?network modules and temporal sub?network modules. Based on the 3D skeleton data obtained from the interactive action video, a spatial action graph of double human interactive action was generated for the representation of spatial information. In the graph, the connecting edges between double human were given different weights according to the joint point position information. The connection of context time information was added in the constructed adjacency matrix, and the joint points in the graph were connected with their nodes within a certain time range in time information processing. The generated spatial?temporal graph data was sent to the spatial graph convolution network module, and the temporal graph convolution network module was combined to enhance the continuity of inter frame motion features for modeling in time. The model fully considers the close relationship of double human interaction. The comparative experimental results on NTU?RGB+D dataset show that the algorithm has strong robustness and obtains better interaction recognition effect than the existing models.

For a long time, fabric defect detection has been completed by quality inspectors. Meanwhile, the process of defect discrimination is greatly affected by subjective factors and has the problems of low detection efficiency and high cost. With the close combination of computer vision technology and various fields, fabric defect detection system based on vision has gradually become an important solution to replace manual quality inspection. For the fabric defect detection based on vision, this paper reviews the aspects including industry development, general detection standards, overall structure of the system and key technologies in detection algorithms, introduces the existing fabric defect detection products based on vision in the market, analyzes the common defect detection standards and the basic structure of the detection system, and summarizes and compares the research status of image processing and deep learning technology in the field of fabric defect detection in recent years. Finally, the paper summarizes the key problems to be solved, and discusses the possible development direction in the future.