3results about How to "Improve segmentation quality" patented technology

This invention discloses an automatic method and system for extracting landslide areas from remote sensing images based on few-sample enhancement and multimodal fusion. The method includes: acquiring and preprocessing multimodal remote sensing data; constructing a foreground-aware few-sample enhancement module, expanding the training samples through geometric transformation, spectral perturbation, and random cropping enhancement strategies; constructing a dual-branch feature extraction network to extract spectral and topographic features respectively; achieving adaptive fusion of spectral and topographic information through a cross-modal attention fusion unit; training a deep learning model using a composite loss function including cross-entropy loss, Dice loss, and boundary constraint loss; and performing topographic constraint post-processing and morphological optimization on the model output to obtain the automatic extraction result of landslide areas. This invention can achieve high-precision landslide identification under limited sample conditions, effectively reducing the false detection rate and improving the model's generalization ability and adaptability to complex scenes.

A multi-beam point cloud target detection method and device based on attention mechanism and geometric prior is disclosed. Belonging to the fields of underwater computer vision and 3D point cloud processing technology, this method includes a two-stage target detection network based on PointNet++. In the first stage, an elevation attention mechanism is introduced to suppress negative ground samples, significantly reducing excessive computational resource consumption and feature redundancy. In the second stage, a local geometric feature enhancement module is introduced to compensate for the loss of structural information caused by feature aggregation in traditional PointNet++ operators, enhancing the model's ability to characterize subtle geometric features and achieving an effective balance between high-precision target recognition and real-time processing performance.

This invention discloses a 3D mesh segmentation method based on boundary awareness and contrastive learning, relating to the field of 3D segmentation in computer graphics and 3D vision. The method includes the following steps: data acquisition, labeling the segmented regions of the constructed 3D mesh, assigning a single integer label to each segmented region, labeling each edge of the 3D mesh with a category, forming a dataset from several labeled 3D meshes, and dividing the entire dataset into a training set and a test set; boundary determination and training a deep learning network using a contrastive learning method; model segmentation, obtaining the trained model, inputting other 3D meshes into the model, and finally outputting the mesh segmentation result. This method can improve the quality of mesh boundary segmentation results, making the boundary regions more accurate.