The detection of magnetic tile quality is an essential link before the assembly of permanent magnet motor. In order to meet the high standard of magnetic tile surface defect detection and realize the rapid and automatic segmentation of magnetic tile defects, a magnetic tile surface defect segmentation algorithm based on cross self-attention model (CSAM) is proposed. It adopts high-low level semantic feature fusion method to build the dependency relationship between the deep and shallow features. Multiple auxiliary loss functions are used to constrain the network and reduce the noise in the deep features. In addition, an image enhancement method is also designed to solve the problem of insufficient annotated data. The experimental results show that the network can achieve 79.6% mIoU and 98.5% PA, which can meet the high standard requirements of magnetic tile manufacturing.

.The semantic segmentation of point clouds has achieved significant progress in recent years. However, most current methods rely on complex stacking of modules to learn local features, resulting in a complicated network structure. We propose an adaptive offset self-attention network (AOSANet), a simple and efficient point cloud learning framework for classification and segmentation tasks. AOSANet has permutation invariance when processing three-dimensional point cloud data and can dynamically learn local features based on the input features. To better fuse the local features, we introduce the maximum feature extraction module, which performs maximum feature weighting prior to local feature fusion. This allows the network to concentrate on the most important features and improve overall robustness. Through extensive experiments on publicly available datasets, the proposed method achieves a state-of-the-art performance on point cloud classification and segmentation tasks.