5results about How to "Improve reconstruction effect" patented technology

The method belongs to the technical field of panoramic monitoring of ultra-high voltage converter station, and aims to solve the problem that panoramic monitoring image data is directly uploaded to the cloud, occupying a large amount of cloud resources. By adopting multi-scale convolution blocks in the deep multi-scale residual network model to construct low-order and high-order features of images of various scales, the incomplete phenomenon of image detail extraction is avoided, and the residual learning mechanism is adopted to retain low-order rough features, thereby improving the reconstruction ability of the image. Topology optimization constructs the topology structure of the heterogeneous network, and the framework combining deep reinforcement learning and Monte Carlo tree search is used to construct the network according to the pre-defined topology rules. The search result of the Monte Carlo tree strengthens the learning of the deep convolutional neural network, so as to obtain more accurate prediction in the next iteration. After the data is processed in the edge side, it is transmitted to the cloud storage, saving the cloud storage space and transmission bandwidth.

A method and device for training a behavior recognition model, an electronic device, and a storage medium are disclosed, relating to the technical field of model training, and including: obtaining a sample image of a driver, and determining a training data set according to the sample image; inputting the training data set into the behavior recognition model for recognition, obtaining a sample recognition result, and determining a recognition confidence according to the sample recognition result and an actual behavior result; if the recognition confidence is less than or equal to a confidence threshold, determining a target loss function value according to the sample recognition result and the actual behavior result, updating the behavior recognition model based on the target loss function value, and training the updated behavior recognition model. The present application can adaptively generate a reorganization kernel according to semantic content, thereby realizing more fine and semantically consistent feature reconstruction, and further ensuring the training accuracy of the behavior recognition model, and further improving the behavior recognition accuracy of the in-vehicle personnel by the trained behavior recognition model.

The invention relates to the technical field of medical image processing, in particular to a CT image reconstruction method based on a progressive texture perception diffusion model, and the method comprises the steps: inputting a to-be-reconstructed low-dose CT image or a sparse view angle image and projection image data into a trained progressive texture perception diffusion model, and outputting a reconstructed CT image; the progressive texture perception diffusion model carries out preliminary reconstruction on a to-be-reconstructed low-dose CT image or a sparse view angle image to obtain a low-frequency structure of the image; based on the low-frequency structure, high-frequency details are generated through conditional Schrodinger bridge diffusion network iteration, multi-scale high-frequency features in the high-frequency details are extracted, and a reconstructed CT image is generated based on the low-frequency structure of the image and the multi-scale high-frequency features. The invention provides a CT (Computed Tomography) reconstruction method based on coarse-fine segmentation, so that artifacts are removed and more tiny tissue textures are reserved on the premise of reserving global structures such as key bones and blood vessels.

The method for super-resolution reconstruction of panoramic monitoring image of protection system of extra-high voltage converter station belongs to the technical field of power equipment detection, and solves the problems of unclearness and low resolution of the existing panoramic monitoring image, which cannot meet the demand of panoramic monitoring of inspectors; by adopting multi-scale convolution blocks to construct low-order and high-order features of images of various scales in the deep multi-scale residual network model, the phenomenon of incomplete extraction of image details is avoided, the residual learning mechanism is adopted in the network model to retain low-order rough features, reduce the training difficulty, promote the reuse of features, and thus improve the reconstruction capability of the image; the reconstructed image has better structural similarity and peak signal-to-noise ratio performance; the standard data set and the panoramic monitoring image data set of the extra-high voltage converter station are used in sequence for image super-resolution reconstruction and target recognition experiment, and the experimental results show that the high-resolution image reconstructed by the method can meet the demand of panoramic monitoring of inspectors.

The embodiment of the application provides a kind of double hand-hinged object pose estimation method and related equipment, belong to computer vision technical field.The method includes: extracting the feature of double hand and object from input image and encoding as joint feature;From global feature, the left hand and right hand position query representing double hand area are predicted and extracted;Interact with double hand position query, generate double hand relative position query representing the relative position relationship of double hand, and combine it with the hand parameter query and object parameter query obtained by mapping into joint query;Using attention masking mechanism, make joint query and joint feature interact, match the most relevant feature information for each query;Finally, the parameters of both hands, the parameters of the object and the hinge angle correction amount are added to obtain the final hinge angle, and the three-dimensional grid of the double hands and the hinged object is output.The application significantly improves the accuracy and rationality of pose estimation by explicitly modeling the correlation between the relative position of the hands and the hinged state of the object.