7results about How to "Guaranteed Segmentation Accuracy" patented technology

The invention relates to a ball head rod disordered grabbing method based on 3D vision, belongs to the technical field of robot operation and automatic manufacturing, and particularly relates to a workpiece pose estimation method based on combination of YOLOv8 and Triplet Attention modules, which is used for disordered grabbing tasks in complex scenes. According to the method, the color and depth images of the workpiece are acquired through the RGB-D camera, and the efficient lightweight Triplet Attention module is introduced, so that the processing speed and precision of the model on the target detection and segmentation task on the mobile terminal equipment are remarkably improved. And the Triplet Attention module enhances the space attention mechanism of the model through three-way rotation convolution operation on the features, so that the detection and segmentation precision is remarkably improved. The segmented mask region is applied to the depth image, corresponding point cloud data are extracted, and six-degree-of-freedom pose estimation is carried out by using a normal consistency optimization (NDT) algorithm. The NDT algorithm is excellent in processing noise and partially shielded scenes. Experimental results show that compared with an original YOLOv8 model, the model added with the Triplet Attention module is improved in segmentation precision and recognition accuracy. The method has high accuracy and robustness in the aspects of workpiece recognition and pose estimation, and is suitable for real-time industrial application scenes.

This invention relates to the field of medical assistance technology, specifically to a pneumonia medical assistance system based on an improved VMamba model. The system includes: dividing the lung image to be diagnosed into raster cells based on a raster cell partitioning algorithm; calculating the lesion representation coefficient of each raster cell within each management time period; calculating the lesion representation index of the lesion trend path within the management period based on the raster cells along the lesion trend path; determining whether the raster cells covered by the lesion trend path are lesion regions based on the lesion representation index; inputting the lung field feature map and semantic feature vector into the improved VMamba model after channel-level concatenation, and outputting the pneumonia classification diagnosis result; and linking the pneumonia classification diagnosis result with a pneumonia knowledge graph to generate a visual auxiliary diagnosis report, significantly improving the accuracy and stability of lung medical image processing and achieving full-process automation of intelligent image analysis.

This application discloses an underwater multi-task target recognition method and related apparatus, comprising: inputting an acquired underwater image into a pre-trained target recognition model; performing multi-layer feature extraction on the underwater image to obtain multiple enhanced feature maps of different scales; the enhanced feature maps of different scales correspond to target detection ranges of different sizes; performing feature fusion processing on the multiple enhanced feature maps to obtain a fused feature map corresponding to candidate targets in the underwater image; the candidate targets are regions in the image where objects exist, including detection targets and segmentation targets; performing decoupled prediction processing on the fused feature maps to output the category prediction results of the candidate targets, the bounding box position parameters corresponding to the candidate targets, and the instance segmentation mask information corresponding to the candidate targets; classifying and outputting the prediction results according to the candidate target category; wherein, for detection targets, the target bounding box and category information are output, and for segmentation targets, the instance segmentation mask and category information are output.

The application belongs to the technical field of computer vision and water conservancy monitoring, and discloses a crack image segmentation method and system based on a light segmentation network, which comprises the following steps: using an encoder to extract multi-level crack features from preprocessed crack image data, and optimizing deep crack features in the extracted multi-level crack features; inputting the optimized multi-level crack features into a decoder to perform up-sampling processing, fusing the up-sampling result with crack features of a corresponding encoding level in the encoder, and obtaining output crack features of each level; generating multi-scale preliminary crack segmentation results according to the optimized multi-level crack features and the output crack features of each level, and optimizing the preliminary crack segmentation results by using a light U-shaped network; and obtaining final crack feature results. The application reduces the model parameter quantity and the calculation cost, and is more suitable for deployment and use on terminal devices with limited computing power.

The application discloses a kind of multi-modal three-dimensional MRI segmentation methods, systems and media based on non-empty voxelization and geometric perception attention, the method includes the following steps: in input stage, by non-empty voxelization to multi-modal MRI body data is screened, and sparse voxel is obtained;In the coding stage, based on three-way dynamic non-empty voxel and geometric perception cross-attention downsampling, the feature of sparse voxel is extracted;In the decoding stage, based on geometric perception cross-attention upsampling, the sparse voxel feature obtained by extraction is realized cross-scale alignment and fusion gradually;In the output stage, the segmentation result is output by convolution. The application realizes while guaranteeing segmentation accuracy, reduces the consumption of computing resources, enhances geometric perception ability and improves the generality of model.

This invention relates to the field of ultrasound robot technology, and more particularly to an intelligent recognition method and system for ultrasound robots based on an AI-powered large-scale ultrasound model. The method includes the following steps: acquiring ultrasound video sequences collected during ultrasound robot scanning and labeling them to obtain raw training data; enhancing the raw training data using a difficult-example data augmentation strategy to obtain enhanced training samples, and then evaluating and filtering these samples through a teacher network to obtain augmented training data; constructing a student network and inputting the augmented training data into the student network for training to obtain a student network model; inputting the test ultrasound video sequence into the teacher network, determining the current target organ based on the multi-organ confidence scores output by the teacher network, switching to the corresponding organ's student network model for real-time segmentation and inference, and outputting the segmentation result. This invention achieves lightweight real-time segmentation and recognition in ultrasound robot scanning scenarios, effectively improving the output stability between adjacent frames.

The present application relates to a kind of single-cell image segmentation methods based on minimum circumscribed circle, it solves how to improve the technical problem of the precision, robustness, adaptability of image-based single-cell segmentation method, first obtain original cell image, secondly, original cell image is converted into gray image, again, gray image is preprocessed, binarization is handled to obtain binary image, then the cell edge contour image is obtained by processing, then the minimum circumscribed circle is drawn for each contour in cell edge contour image to obtain cell circumscribed circle image, finally, with the center of minimum circumscribed circle as the center point of clipping region, with the standard of containing cell and the background in a certain range around cell periphery to determine the boundary of clipping region, cell circumscribed circle image is cropped, and finally single-cell image is obtained.The present application is suitable for single-cell identification, segmentation in microscope image, can be widely applied in cell biology research, medical diagnosis, drug screening and other fields.