4results about How to "Improve feature utilization" patented technology

The application provides a space target recognition method and device based on an attention mechanism adaptive enhancement. The method comprises the following steps: acquiring a space target image; inputting a tensor of the space target image into a pre-trained space target recognition model to obtain a recognition result of the space target. For any one processing stage submodel, a tensor processing module set unit and a convolution block attention module are included. In the tensor processing module set unit, a plurality of tensor processing modules are connected in sequence, the tensor processing module is used for convolution and channel transformation of the input tensor, and / or transformation processing of the tensor element value. According to the technical scheme of the application, the tensor processing module is used for convolution and channel transformation of the input tensor, and / or transformation processing of the tensor element value, the accuracy of the final recognition result can be significantly improved, the convolution block attention module realizes channel screening, is conducive to focusing on key features, and improves the feature utilization rate.

The application discloses a kind of based on multi-view fusion's occlusion target detection method and system, belong to computer vision technical field, it is applicable to intelligent monitoring, automatic driving etc. Scene.For the problems of inaccurate cross-view alignment, inefficient fusion, fuzzy occlusion recognition and poor real-time performance in the prior art, the present application synchronously collects images and calibrates parameters through at least 2 devices;After adaptive filtering and distortion correction preprocessing, enhanced features are extracted using improved ResNet50 containing CBAM;Based on Kalman filtering dynamic alignment cross-view features, identify the occlusion area by combining feature missing rate, similarity and multi-dimensional index of depth difference;Optimize features using hierarchical fusion strategy and input improved YOLOv8 containing occlusion branch for detection.The alignment error of the present application is not more than 2 pixels, the occlusion recognition accuracy is not less than 92%, the detection accuracy is not less than 95% and the frame rate is not less than 20fps, effectively solving the bottleneck of occlusion scene detection, adapting to latency-sensitive applications.

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 invention provides a method and related equipment for measuring the temperature inside a top-blown tin smelting furnace. The method involves reorganizing one-dimensional time-series data from a source domain dataset to obtain a two-dimensional dynamic matrix. A multi-scale parallel convolutional architecture is then pre-trained to obtain a first furnace temperature prediction model. Knowledge transfer is performed on this first model using a target domain dataset to obtain a second furnace temperature prediction model. Real-time collected operating data of the top-blown tin smelting furnace is compared with historical operating data in the target domain dataset to obtain a fine-tuned dataset. This fine-tuned dataset is then used to fine-tune the second furnace temperature prediction model to obtain a third furnace temperature prediction model. Historical operating data of the target top-blown tin smelting furnace is input into the third furnace temperature prediction model for prediction, yielding a future furnace temperature prediction. This method can improve temperature measurement accuracy even when labeled data in the target domain is extremely scarce.