10results about How to "Reduce inference time" patented technology

The application provides a large model inference acceleration method, device, equipment, storage medium and program product, and relates to the technical field of artificial intelligence, and the method comprises the following steps: inputting an interactive prompt word and historical dialogue into a large language model for self-recurrence decoding to obtain a current word element; it is detected whether the current word element is a text operation semantic word element; if the current word element is a text operation semantic word element, a target text segment is copied from existing text information based on the text operation semantic word element; a plurality of target word elements are updated to the end of the output text; based on the large language model, self-recurrence decoding is continued to generate a next word element, the next word element is taken as the current word element, and the step of detecting whether the current word element is a text operation semantic word element is returned to until the large language model generates an end symbol, and an updated output text is obtained. Through the above manner, the text copying efficiency is improved, the inference acceleration effect of the large language model is optimized, and the accuracy and stability of the copying operation are beneficially ensured.

This invention discloses a lightweight joint estimation method for battery state of charge (SOC) in resource-constrained MCUs, comprising: extracting the weight matrices and bias terms of each layer of a trained LSTM time-series estimation network and flattening them into a one-dimensional static constant array; constructing a custom pure C language matrix operation library independent of a third-party AI inference framework on the target microcontroller, employing a global static memory pool management strategy, and defining a unified structure containing the number of matrix rows, columns, and one-dimensional floating-point data pointers at the C language level; the target microcontroller calling the custom pure C language matrix operation library to directly read the one-dimensional static constant array to complete the forward inference calculation of each gate unit of the LSTM network and output the prior SOC observation value; and calling the custom pure C language matrix operation library to perform state equation iteration of the Kalman filter and output the SOC estimation result. This invention enables the safe and efficient operation of complex coupled algorithms on a very low computing power platform.

The application provides a multi-scene safety inspection method and system based on a UAV, and belongs to the technical field of UAV safety inspection, and comprises the following steps: constructing a multi-scene safety sample library; training a multi-task computer vision model based on the multi-scene safety sample library to obtain a safety inspection algorithm model integrating multiple recognition capabilities; the multi-task computer vision model comprises a shared feature extraction backbone network and multiple independent task head networks; a training set is input into the shared feature extraction backbone network to extract general feature maps; the general feature maps are respectively input into the multiple independent task head networks for parallel training to obtain the safety inspection algorithm model; and the safety inspection algorithm model is loaded on a UAV flight platform to perform parallel inference analysis on inspection images collected by the UAV in real time, and to identify preset safety hidden danger targets.

The application provides a SAR target scattering center parameter fast extraction method, and mainly solves the problems of slow speed and low precision of a traditional SAR target scattering center parameter extraction method; firstly, the application models a scattering center parameter solving problem based on a sparse representation theory; then, a SAR target scattering center parameter extraction problem model based on a semi-quadratic splitting method is constructed, a deep unfolding network is constructed according to an optimization process based on the semi-quadratic splitting method, and the SAR target scattering center parameter is extracted by using the deep unfolding network; compared with a traditional method, the method constructs a deep unfolding network based on the semi-quadratic splitting method and a scattering center model, and realizes efficient and interpretable SAR target scattering center parameter extraction.

This invention discloses a 3D multi-object tracking method based on spatiotemporal adaptive attention. The method includes: acquiring 2D multi-source data and 3D multi-source data of a target scene; extracting image features from the 2D multi-source data to obtain image features, and extracting point cloud features from the 3D multi-source data to obtain point cloud features; fusing the image features and the point cloud features to obtain fused features; determining the detection state of each object in the target scene based on the fused features; extracting cooperative features between the detected object and the predicted object based on the detection state; and updating the object detection and tracking state in the target scene in real time based on the cooperative features. This invention can effectively achieve better tracking performance and reduce inference time, and can be widely applied in the field of computer technology.

This invention discloses an interpretable low-light image enhancement method. It constructs an unpaired learning framework, EDC-Net, with enhancement and degradation branches forming a closed loop. The enhancement branch adaptively enhances the brightness and contrast of the low-light image through pixel-level affine mapping, while the degradation branch predicts physically meaningful degradation parameters such as color gain and exposure scaling. The enhanced image is then reconstructed into a low-light image through a chain of explicit degradation operators, forming a cyclic consistency constraint. Training employs a three-stage progressive optimization strategy: degradation warm-up, master adversarial training, and fine-tuning. During the inference phase, only the lightweight enhancement branch is run. This invention achieves physical interpretability and controllability of the enhancement process, avoids overexposure and artifacts, improves training convergence stability, and significantly increases inference efficiency. It is suitable for real-time image processing scenarios such as nighttime surveillance and autonomous driving.

The invention provides an event camera real-time deblurring method based on dynamic edge guidance. The event camera real-time deblurring method comprises the following steps: acquiring a to-be-recovered blurred image and event stream data in the same field of view; extracting an image modal feature and an event modal feature by using a double-flow visual encoder; constructing a dynamic edge guiding module, predicting pixel-level multi-level fuzzy probability distribution, and generating a dynamic space edge priori graph; removing redundant background events by using an edge prior graph, and screening out a key event Token rich in motion information; and constructing a cross-modal fusion module based on bidirectional RWKV, carrying out long-distance space-time modeling on the screened event Token and image features with linear calculation complexity, and finally decoding and outputting a clear image. According to the method, the sensor noise is effectively suppressed and the calculation redundancy is reduced under the condition of not depending on the traditional hard gating through a'guide-selection 'normal form, and the image deblurring which is high in signal-to-noise ratio and high in structural similarity and meets the real-time requirement is realized.

The invention provides an autonomous navigation laser radar-camera fast fusion target detection method and device and a storage medium, and belongs to the field of multi-modal target detection, and the method comprises the steps: obtaining a laser radar point cloud and a camera image; obtaining point cloud BEV features according to the laser radar point cloud; performing feature extraction on the camera image to obtain a first image feature, and converting the first image feature into an image BEV feature by using a dynamic view angle converter; fusing the point cloud BEV features and the image BEV features to obtain fused BEV features; and a final target detection result is obtained. According to the invention, the operand of BEVPooling is reduced through the dynamic view angle converter, and the time and space complexity of the network structure is reduced, so that the reasoning time of the laser radar-camera fusion perception neural network is reduced, and the real-time performance of target detection is improved.

The application provides a real-time target detection method in an ultrahigh-resolution image scene, comprising: processing an original image to generate a high-resolution image and a low-resolution image; processing the low-resolution image to obtain a reserved area; cutting the high-resolution image according to the reserved area to generate a plurality of regional subgraphs; and detecting the regional subgraphs to obtain a detection result. The application can quickly detect the area requiring further fine-grained detection from an ultrahigh-resolution image through resolution processing, multi-scale density regression, area determination and area generation processing. In addition, due to the scale normalization, target detection and result merging processing, the final ultrahigh-resolution detection result can be generated. In the target detection stage, the plurality of regional subgraphs can be processed in parallel, which can greatly improve the inference time. Since part of the object-free area can be discarded in the density estimation stage, the required storage resources and calculation time can be saved.

The invention relates to a rapid pesticide residue screening method based on an optimized electronic nose array. The rapid pesticide residue screening method comprises the following steps: selecting a plurality of gas sensors made of different sensitive materials to form a basic sensor group, and performing array arrangement optimization; grading the to-be-detected agricultural product sample; synchronously collecting resistance change signals and response peak value occurrence time of the sensor in a specified time period; performing baseline correction on the dynamic response data, and then removing environmental interference noise; screening out specified key feature parameters; dividing the screened key feature parameters into a training set, a verification set and a final feature set according to a specified proportion, and performing training optimization verification on a fusion model of the support vector machine and the random forest; inputting the final feature set into the optimized screening model, and outputting a positive / negative judgment result and confidence; and respective processing is carried out according to the division of the confidence. Through sensor array optimization, pretreatment, multi-dimensional data preprocessing and fusion model construction, rapid and accurate screening of pesticide residues is realized.