The typical defect detection algorithm is ineffective due to the contrast between the magnetic tile defect and the various defect features. An improved YOLOv5‐based algorithm, for detecting magnetic tile defects with varying defect features, is suggested. The procedure begins by incorporating the CBAM into feature extraction network of YOLOv5. It improves the feature of network learning capabilities for the target region by filtering and weighting the feature vectors in such a way that the processing of network is dominated by the essential target characteristics. A new loss function of detection model is then proposed according to the properties of the magnetic tile picture, and the confidence of prediction box is increased. Data augmentation technologies are introduced to increase the number of data samples. Based on magnetic tile defect datasets, the evaluation results have shown that the precision of the proposed approach is 98.56%, 3.21%, and 7.22% greater than the original YOLOv5 and Faster R‐CNN, respectively, all of which demonstrate the effectiveness and accuracy of the proposed method.

In recent years, holographic projection technology has been integrated into the real society, which brings a new visual experience to people and meets their high requirements for audiovisual experience. With the development of holographic technology, the hologram from holographic technology has also attracted people’s attention. The application of holograms in the fields of art, science, and technology conforms to the trend of the times and will become an indispensable part of people’s life in the future. In this paper, it mainly studies the hologram to analyze its algorithm and production process and its application prospects.

Network alignment is an important technique with applications in diverse domains, such as social network analysis, bioinformatics, and knowledge graph construction. Many of the alignment methods rely on predefined anchor nodes, which are often unavailable in real-world scenarios. To overcome this limitation, we propose MANNA (MAtched Neighbor consistency for Network Alignment), an unsupervised approach to network alignment that exploits the concept of Matched Neighborhood Consistency (MNC). The hypothesis of MANNA is that nodes with higher similarity within their local neighborhood structures are more likely to be aligned across different networks. To learn the structural and attribute features of networks, MANNA uses a Graph Neural Network (GNN). It extracts multi-order node embeddings to capture multi-scale neighborhood features, which are then used to construct similarity matrices for the alignment process. MANNA introduces a key innovation by using pseudo-anchor nodes identified by the MNC strategy to provide self-supervised learning signals in the absence of real anchor nodes. This approach enhances the model’s ability to learn accurate network representations and improve alignment accuracy. Alignment results are iteratively refined by applying the MNC strategy, which strengthens the consistency of neighborhood structures between matched nodes. Extensive experiments on three public datasets show that MANNA outperforms existing network alignment methods.