Metallic surface defect detection is critical to ensure the quality of industrial products. Recently, human-advanced surface defect detection algorithms have been proposed. Most of these algorithms rely on convolutional neural networks (CNN) and an anchoring scheme. However, a convolution unit only samples the input feature maps at fixed shapes and locations. Similarly, a set of anchors are uniformly predefined with fixed scales and shapes, which increases the difficulties of bounding box regression. Therefore, we propose an adaptive convolution and anchor network for metallic surface defect detection, named ACA-Net. Specifically, an adaptive convolution and anchor (ACA) module is proposed, which mainly consists of adaptive convolution and an adaptive anchor. Firstly, an adaptive convolution module (ACM) is designed, which adaptively determines the location and shape of each convolution unit. In addition, a multi-scale feature adaptive fusion (MFAF) is proposed, which is used in ACM to extract and integrate multi-scale features. Then, an adaptive anchor module (AAM) is proposed to yield more suitable anchor boxes by adaptively adjusting shapes. Extensive experiments on NEU-DET dataset and GC10 dataset validate the performance of the proposed approach. ACA-Net achieves 1.8% on NEU-DET dataset higher Average Precision (AP) than GA-RetinaNet. Furthermore, the proposed ACA module is also adopted in GA-Faster R-CNN, improving the AP by 1.2% on NEU-DET dataset.

To the problem of the complex pre-processing and post-processing to obtain head-position existing in the current crowd localization method using pseudo boundary box and pre-designed positioning map, this work proposes an end-to-end crowd localization framework named WSITrans, which reformulates the weakly-supervised crowd localization problem based on Transformer and implements crowd counting. Specifically, we first perform global maximum pooling (GMP) after each stage of pure Transformer, which can extract and retain more detail of heads. In addition, we design a binarization module that binarizes the output features of the decoder and fuses the confidence score to obtain more accurate confidence score. Finally, extensive experiments demonstrate that the proposed method achieves significant improvement on three challenging benchmarks. It is worth mentioning that the WSITrans improves F1-measure by 4.0%.

Compared to traditional platform environments in the online realm, the metaverse, as a three-dimensional (3D) virtual world, exposes more identity data to the network. Once these data are compromised, it leads to privacy breaches. Therefore, how to ensure identity security in the metaverse environment has become an urgent problem to be solved. Although research on identity authentication schemes can help improve identity security, traditional identity authentication schemes in network environments are studied based on their own environmental characteristics, which makes it difficult to meet the security needs in the metaverse environment. As a result, in this paper we propose an elliptic curve cryptography (ECC)-based identity authentication scheme to address identity authentication issues in the metaverse environment. This scheme ensures secure communication among users, avatars, and platform servers. The security of this scheme was demonstrated through informal security analysis and the automated validation of internet security protocols and applications (AVISPA) formal security analysis tools, and the results showed that it can resist various known attacks. Compared with existing identity authentication schemes, this scheme has lower computational and communication costs.