7results about How to "Resolution granularity" patented technology

The present application relates to the technical field of image sensor calibration, and particularly relates to a processing method and system of a hybrid event and frame sensor, computer equipment and a medium; the method comprises the following steps: acquiring original data of the hybrid event and frame sensor; performing a noise calibration operation on an intensity frame to generate an active pixel sensor noise model and a net intensity signal; performing an event probability coupling operation on an event stream to generate an event visual sensor event probability model; and performing unified collaborative processing on the active pixel sensor noise model and the event visual sensor event probability model to output uniformly calibrated sensor data. In this way, a unified and reproducible cross-modal calibration framework can be provided to solve the technical problems of existing calibration technologies, such as weak physical coupling, insufficient calibration granularity and black box implementation, and to provide a unified, reproducible and high-precision sensor data calibration output for high-speed and high-dynamic-range mobile perception applications.

This invention belongs to the field of UAV defect detection technology, and discloses a UAV bridge defect detection method and related apparatus based on the CSG-DETR model. The method includes: acquiring bridge surface defect images using a UAV to obtain a UAV bridge defect detection dataset; inputting the trained CSG-DETR model into the UAV bridge defect detection dataset to obtain multi-scale features; performing feature fusion and enhancement mechanisms on the multi-scale features to obtain fused features containing structural and semantic information; generating target-level feature representations through global modeling; and detecting the target category and bounding box of bridge defects based on the target-level feature representations, outputting the UAV bridge defect detection results. This invention achieves the dual goals of improved accuracy and lightweight design, has broad application prospects, is suitable for UAV inspection scenarios, realizes real-time, high-precision detection of bridge surface defects, and reduces the cost and risk of manual inspection.

The present application relates to the technical field of multi-modal data prediction, and discloses a fashion preference prediction method and device, which comprises obtaining multi-modal data including images, texts and user behavior time sequence, performing feature extraction to obtain image features, text features and time sequence features; constructing spatial features based on city embedding tables and corresponding regional culture label codes; aligning the dimensions of the spatial features and the time sequence features, fusing the aligned spatial features and the time sequence features by using a gated attention mechanism to obtain spatio-temporal fusion features; splicing the spatio-temporal fusion features with the image features and the text features to obtain multi-modal fusion features; decoupling the multi-modal fusion features to obtain material decoupling features, style decoupling features and scene decoupling features; and after feature splicing of the various decoupling features, performing Transform coding and MLP classification to obtain fashion preference prediction probability.

The application discloses a pipeline leakage detection method and device, the method comprises the following steps: acquiring video images and pressure data of multiple continuous time steps in a pipeline, fusing the video images and the pressure data to obtain fusion information; performing space-time feature extraction on the fusion information by using a space-time feature extraction network to obtain a feature vector; performing classification prediction on the feature vector by using each classification head in a trained multi-heterogeneous structure classification model to obtain pipeline state categories output by each classification head; and determining a final pipeline state category by using an entropy balance decision function according to the pipeline state categories output by each classification head and weights of each classification head, so that the accuracy of pipeline leakage detection is improved.

The present application belongs to the technical field of special steel forging, and particularly relates to a forging process for a FXM-19 reactor shaft forging. The process is characterized by the following steps: performing surface finishing treatment on raw materials, selecting a suitable heating furnace, designing a reasonable heating process, performing twice forging by using a 1800-ton radial precision forging machine, reasonably designing the deformation process of each heating, and controlling the hammering amount and pulling speed of each pass of the precision forging machine, so as to meet the surface quality and grain size requirements of the FXM-19 reactor shaft forging. The process is suitable for the forging of special steel with a surface prone to cracking and a length not less than 10 meters.

The application discloses a kind of full-view image metering live oil solid deposition test methods in the field of crude oil solid deposition, step 1, image pre-processing;Step 2, image detection and identification;Step 3, man-machine interaction;Compared with prior art, the beneficial effects of the present application are that the image metering and particle size test of the whole process of solid phase particle generation and growth in the process of crude oil high pressure physical property test are solved, the traditional solid deposition can only be qualitatively analyzed, the quantitative analysis of the whole process and full scale of solid deposition particles is achieved, the image metering and particle size test of the whole process of solid phase particle generation and growth in the process of crude oil high pressure physical property test are solved, the traditional solid deposition can only be qualitatively analyzed, the quantitative analysis of the whole process and full scale of solid deposition particles is achieved;The geometric properties of solid phase can be greatly reduced by using computer vision technology to automatically and quantitatively analyze the solid phase, and the work efficiency is improved.

The present application relates to the technical field of model training learning, in particular to a training method of an intent recognition model for understanding user demand in a text-to-software scenario, comprising the following steps: traversing a path to determine a level and establish a variable dimension projection mapping, generating a semantic orthogonal conflict sample, obtaining a variable dimension feature by using matrix transformation, quantifying a sibling node overlap degree to impose an orthogonal constraint, updating a weight by minimizing a positive sample distance and maximizing a conflict sample distance, and constructing a text-to-software intent recognition optimization model.In the present application, a mapping mechanism of a level position and a variable dimension projection matrix is established through a software architecture topology, a semantic orthogonal logical conflict sample is constructed and a sibling node feature branch orthogonal constraint is introduced, the feature representation of parallel architecture branches is forced to separate in a vector space, the distance between logical conflict samples is maximized and the semantic overlap of similar function nodes is eliminated, and a high-differentiation decision boundary is constructed to accurately analyze complex nested instructions.