2301 results about "Video image" patented technology

The present disclosure relates to the field of computer vision and discloses a three-dimensional dynamic scene reconstruction method and apparatus, and a storage medium. The three-dimensional dynamic scene reconstruction method comprises: acquiring synchronized videos of a plurality of viewpoints of a dynamic scene; computing matching points between video images of different viewpoints, and estimating intrinsic and extrinsic parameters of each camera; obtaining a Gaussian splatting point set {p0} on the basis of a sparse point cloud constructed according to the depth of each matching point; for the first image frame of each video, using {p0} to perform static training thereon, to obtain a Gaussian splatting point set {p}; for the remaining image frames, dividing {p} into a static point set {S} and a dynamic point set {D}, performing dynamic training on {D}, and constructing a dynamic Gaussian splatting point set {P} from {p}, {S}, and the final {D}; and, in view of the intrinsic and extrinsic parameters of each camera, rendering {P} using a Gaussian splatting rendering pipeline, to obtain rendered images at different moments from new viewpoints.

The invention discloses a high-precision instrument assembly fault backtracking method and system, belongs to the field of precision manufacturing, and aims to solve the problems that in a traditional backtracking method, assembly data are scattered and unreliable, fault root positioning is fuzzy, and new scene adaptation depends on a large amount of data. The method comprises the following steps: collecting assembly structured data, video images and environment data in a multi-source manner, filtering out low-quality images, and distributing unique identifiers for products; fusing the multi-modal data to generate a depth feature matrix; constructing an anomaly detection model to output a risk score and a label; hashing the data and then storing the data into a product exclusive private block chain; when a fault occurs, extracting data on the chain through a unique identifier, reconstructing an assembly process by using a graph neural network, and comparing a standard positioning root; and based on the fault report incremental training model, parameters are optimized in combination with meta-reinforcement learning. According to the method, the data authenticity is guaranteed, the fault backtracking precision and efficiency are improved, a new scene is quickly adapted, the production rework rate is reduced, and the stable assembly quality is maintained.

The invention relates to the technical field of transformer substation intelligent patrol, and provides a transformer substation three-dimensional fusion patrol method and system based on digital twinborn and autonomous identification. According to the method, a fused three-dimensional model is constructed through multi-source data acquisition and a three-dimensional Gaussian splash algorithm, and in combination with deep learning-based point cloud semantic segmentation and clustering, an equipment-patrol means coverage relationship is generated. Creating a virtual inspection proxy object based on a three-dimensional virtual environment, and controlling terminals such as an unmanned aerial vehicle to collect real-time video image data; the system carries out automatic identification on pictures, automatically completes equipment level alignment and standard point location identification, generates fine control of camera zooming, horizontal rotation, pitching and the like, and realizes standardized view finding and acquisition. By combining an enhanced recognition algorithm, traditional image processing and a deep learning model are fused, model self-evolution is realized through incremental learning, flexible expansion and collaboration of various patrol terminals are supported through a unified interface, and refined, real-time and intelligent patrol operation and maintenance requirements of an intelligent substation are met.

The invention provides a high-altitude operation risk early warning method and system based on camera image recognition, and relates to the technical field of computer vision, and the method comprises the steps: firstly collecting a video image sequence of a high-altitude operation scene, and generating a fusion feature map containing environment and operation main body features through multi-level feature extraction; performing spatial dimension segmentation and regional feature comparative analysis on the fusion feature map to obtain a spatial risk distribution map containing risk region identification information, processing the spatial risk distribution map of continuous frames based on a time sequence feature fusion rule to generate a dynamic risk evolution map, and calling a risk decision model to perform mode recognition to obtain a dynamic risk evolution map; and generating a risk level classification result and a risk position coordinate set according to the risk level classification result and the risk position coordinate set, and finally generating a risk early warning signal and sending the risk early warning signal to the monitoring terminal, thereby comprehensively, accurately and dynamically monitoring the high-altitude operation risk, and improving the accuracy and timeliness of risk early warning.

The invention relates to the technical field of image segmentation, in particular to an intensive care unit video image processing method based on image semantic segmentation, which comprises the following steps of: acquiring a video image through monitoring camera shooting, dividing a semantic region, calculating a pixel displacement direction of each part, identifying an abnormal track, erasing interference, recombining a boundary contour, and comparing shape change. And constructing a trend and re-drawing a structure, evaluating stability in combination with directions, speeds and amplitudes, screening and correcting inconsistent labels, and outputting an index result. According to the method, track features are constructed by introducing pixel direction coding, a deviation region is identified and interference is eliminated in combination with a direction change trend, an edge structure is reconstructed by using a boundary connection sequence and curve fitting, contour division is optimized through deformation direction consistency, and labels are corrected by synthesizing direction change and boundary speed. Dynamic tracking and accurate labeling of the patient state are achieved, the abnormal action recognition efficiency and the label updating accuracy are improved, and the time sequence integrity and the space expression ability of image data are enhanced.

A vehicular camera monitoring system includes an electronic control unit (ECU) at a vehicle and a support arm movably disposed at a side portion of the vehicle, with the support arm having a base end attached at the side portion of the vehicle and a distal end opposite the base end. A camera is disposed at the distal end of the support arm. The support arm is movable between a stowed position and an extended position. A cover element covers an aperture at the side portion at least when the support arm is in the extended position. The camera, when the support arm is in the extended position, captures image data and provides captured image data to the ECU, which processes the provided image data for (i) display of video images derived from provided image data and / or (ii) detection of an object in the field of view of the camera.

The invention discloses an intelligent alarm positioning method and system based on an unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicle monitoring and geographic space information processing, and the method comprises the steps: obtaining a video stream in real time based on the unmanned aerial vehicle, and recognizing a risk point location in a video image; a three-dimensional space positioning model is constructed based on telemetry data and lens angle parameters acquired by the unmanned aerial vehicle in real time. And performing three-dimensional coordinate dynamic solution on the risk point location based on a three-dimensional space positioning model to obtain a world coordinate of the risk point location. And determining and outputting an alarm position of the risk point location based on the world coordinates. An automatic detection and artificial interaction dual-channel mechanism is adopted, an alarm target is accurately identified and positioned in an unmanned aerial vehicle real-time video, a corresponding relation between video pixels and geographic coordinates is established through real-time registration of an unmanned aerial vehicle image and the digital earth, a space coordinate conversion error caused by view angle difference is corrected by using a depth map, and an alarm target is accurately identified and positioned. Accurate conversion from video pixel coordinates to world coordinates is realized, and high-precision positioning support is provided for remote monitoring of the unmanned aerial vehicle.

The invention discloses an intelligent retrieval method and system fusing text and image semantic features. The method comprises the following steps: S1, carrying out quality detection and preprocessing on an image scanning copy of an electronic file and a case simultaneous recording video; s2, constructing a structured electronic file directory; s3, extracting a text semantic feature, a file image semantic feature and a video image semantic feature as multi-modal features of the text and the image; s4, carrying out feature fusion and alignment on the multi-modal features of the text and the image through a multi-modal large model, and generating a cross-modal unified feature vector with semantic consistency; s5, automatically constructing a case knowledge graph, and realizing structured and semantic integration of legal information; and S6, performing semantic analysis and multi-hop reasoning based on natural language query and the case knowledge graph, and generating and presenting a retrieval result in a structured or question and answer form. According to the method, the semantic features of the text and the image are fused, so that deep knowledge mining and efficient intelligent retrieval of the electronic file are realized.

The invention discloses a fire detection method and device based on multi-modal perception and D-S evidence theory fusion. The method comprises the following steps: collecting multi-modal perception big data at least comprising video image data and temperature sensing data in a monitoring area; performing fire visual feature analysis on the video image data to generate first basic probability distribution; performing fire temperature characteristic analysis on the temperature sensing data to generate second basic probability distribution; taking the first basic probability distribution and the second basic probability distribution as two independent evidence sources, and fusing by adopting a D-S evidence theory to obtain a fused third basic probability distribution; converting the third basic probability distribution into a fire occurrence probability for decision making; and when the fire occurrence probability exceeds a preset alarm threshold, determining that a fire occurs and triggering an alarm. According to the fire detection method, multi-modal sensing big data are synchronously collected and fused, multi-source uncertain information is processed and decided under the framework of the D-S evidence theory, and the early stage of fire detection is remarkably improved.

The invention discloses an underground pipeline intelligent detection and mapping method based on image recognition, and the method comprises the following steps: 1, obtaining continuous video images, associating feature matching pairs of adjacent key frames, and forming a pose parameter set; 2, inputting the key frame into an improved YOLO-World detection network, and outputting a detection result set; 3, obtaining a geometric consistency matching set according to the detection result set; 4, performing multi-view triangularization on the geometric consistent matching set to form a weight factor; 5, introducing a weight factor, and executing incremental beam adjustment optimization on the pose parameter set and the three-dimensional sparse point set to obtain a sparse semantic point cloud; and step 6, outputting an underground pipe network topology map. According to the invention, high-precision and high-robustness intelligent identification and topological mapping in a complex underground pipeline environment are realized.

The invention relates to the technical field of video coding and image processing, and discloses a code rate control method and system based on video image segmentation, and the method comprises the steps: carrying out the pixel-level semantic segmentation of a to-be-coded video frame sequence; extracting a foreground region of interest; calculating a corresponding segmentation uncertainty parameter; establishing semantic mutation parameters of the foreground region of interest; generating a semantic perception weight; executing region-level target code rate redistribution and quantization parameter mapping; and executing partition coding control. In the prior art, code rate control mainly depends on motion intensity or pixel complexity, and especially when a foreground target suddenly appears or disappears in a monitoring scene, a technical problem that a key target is blurred or a background code rate is wasted is easily caused. Due to the fact that the uncertainty modeling and semantic mutation sensing mechanism of the semantic segmentation result is introduced, priority coding of the foreground interest area is achieved under the frame-level code rate constraint condition, and the video coding quality and the code rate utilization efficiency are improved.

The invention discloses a video transmission image stitching data enhancement method based on deep learning, and relates to the field of video image processing. The method comprises the following steps: S1, acquiring and screening images; s2, continuously screening structures and selecting key frames; s3, correcting image distortion; s4, splicing and fusing the images; s5, performing image enhancement output; firstly, a sliding time window mechanism is adopted, multi-dimensional image quality screening is combined, fuzzy, underexposure or severely-shielded inferior frames are accurately removed, and high quality of input key frames is ensured; through intelligent splicing and enhancement of a dynamic adaptive threshold strategy and semantic guidance, the image splicing precision and efficiency of the unmanned aerial vehicle and the multi-view camera in a complex environment are remarkably improved; besides, semantic segmentation guided feature extraction is combined with a multi-band fusion technology, seamless splicing is realized, the quality of an output image is improved, and the reliability of automatic analysis and decision making is remarkably improved.

The invention discloses an HDR video reconstruction method based on a standardized stream, and belongs to the technical field of high dynamic range image processing. The method comprises the following steps of: firstly, constructing a convolution optical flow estimation module with a self-adaptive normalized structure, wherein the convolution optical flow estimation module is used for accurately acquiring optical flow information between adjacent frames in an alternative exposure LDR video image sequence; then, carrying out multi-level feature alignment on the image sequence through an image alignment module so as to reduce alignment errors caused by illumination difference and movement; and finally, inputting the aligned and fused multi-level LDR image features into a standardized flow reconstruction network to realize high-quality HDR video image reconstruction. Aiming at the video reconstruction problem under the alternate exposure condition, the invention designs a standardized flow modeling structure considering the optical flow estimation precision and the feature alignment effect, and effectively improves the HDR video reconstruction quality in a complex dynamic scene.

The invention discloses a water supply and drainage pipeline anomaly detection method based on image recognition. Initial video image data are acquired through an image sensor; establishing a three-channel feature extraction network model, wherein the three-channel feature extraction network model at least comprises a visible light channel, a geometrical shape channel for extracting pipeline structure deformation features through 3D convolution and a dynamic optical flow channel for capturing liquid flow abnormal features based on an RAFT algorithm; inputting an initial video image into the three-channel feature extraction network model to perform feature extraction, establishing an analysis model based on a bidirectional LSTM neural network, inputting feature image data into the analysis model to establish time sequence association, performing abnormal evolution through a GNN graph neural network, performing abnormal type classification according to a pipeline state evaluation index, and obtaining a pipeline state evaluation result. And performing early warning based on a classification result. The detection efficiency is improved, the labor cost and the safety risk are reduced, and operation and maintenance personnel can master the state of the pipeline in time.

The present application relates to a geographic mosaicking method and apparatus for video images, and a computer device and a storage medium. The method comprises: acquiring real-time live streaming images of a real-world scene that are collected by a plurality of unmanned aerial vehicles, and GNSS information of the plurality of unmanned aerial vehicles; using a visual SLAM algorithm to perform input frame tracking on the real-time live streaming images, and performing pose estimation on successfully tracked input frames by combining visual trajectories and the GNSS information, so as to generate georeferenced camera poses; using the camera poses and a surface reconstruction algorithm to perform densification processing on the input frames, so as to generate a depth map of the real-world scene, and mapping the depth map into dense three-dimensional point clouds, so as to construct a three-dimensional surface model of the real-world scene; and using the camera pose and the three-dimensional surface model to perform orthorectification on the input frames, and integrating the orthorectified input frames into a global mosaicked map, so as to generate a global image of the real-world scene. By means of the embodiments of the present application, a real-time picture of a real-world scene can be quickly acquired, thereby providing robust support for a rapid emergency response in a real-world scene.

The invention provides a pulmonary nodule display method and device, electronic equipment and a storage medium, and the method comprises the steps: segmenting a three-dimensional reconstruction image of the chest of a target patient to obtain a preoperative segmentation image, the three-dimensional reconstruction image being obtained based on preoperative CT data, and the preoperative segmentation image comprising the position information of a pulmonary nodule; segmenting the video image of the intraoperative lung tissue of the target patient collected by the thoracoscope in real time to obtain an intraoperative segmented image; feature matching is conducted on the preoperative segmented image and the intra-operative segmented image, the lens pose of the thoracoscope is determined, the preoperative segmented image is mapped into a two-dimensional reference image based on the lens pose, and the two-dimensional reference image comprises the position information of the pulmonary nodule; and carrying out image registration on the two-dimensional reference image and the intra-operative segmented image, and carrying out pulmonary nodule marking on the intra-operative segmented image to obtain a video image for displaying pulmonary nodules in real time. The position of the pulmonary nodule in the operation is accurately displayed in real time in a non-invasive mode, and the operation efficiency is improved.

The invention relates to the technical field of fire early warning, in particular to an early fire early warning system based on AI image recognition, and the system comprises an image collection module which is used for obtaining the video stream data of a monitoring area in real time; an AI image analysis module which is in communication connection with the image acquisition module and is used for receiving the video stream data and carrying out real-time analysis on video frames based on a pre-trained fire identification model so as to extract visual features related to the fire; and the early warning judgment module is in communication connection with the AI image analysis module and is used for receiving an analysis result of the visual features. According to the early fire early warning system and method based on AI image recognition, through video image analysis, the system can recognize weak flame or smoke characteristics at the initial stage of a fire and when naked eyes do not obviously see the characteristics, and the delay problem that a traditional smoke-sensing and temperature-sensing detector needs to wait for physical parameters to be diffused to the detector to be triggered is solved.

The invention relates to the technical field of facial dynamic video image pain assessment, in particular to a facial dynamic video image pain assessment method based on a dynamic fusion module, and the method comprises the steps: collecting data through a multi-modal sensor, and generating a three-dimensional feature mapping map; activating an adaptive weight adjustment module to generate a configuration parameter set; executing micro-expression feature extraction and dynamic fusion operation to output a high-precision feature vector; inputting a grading evaluation model to complete pain degree quantitative grading. According to the method, facial micro-expression changes can be accurately captured, feature distortion can be dynamically recovered, nonlinear feature components can be mined, feature extraction integrity and evaluation accuracy can be improved, meanwhile, the feature capture capability in a complex environment can be enhanced through a controlled gradient optimization dynamic fusion technology, and the calculation efficiency can be optimized.

The invention relates to the technical field of artificial intelligence, and particularly provides a multi-modal information fusion emotion detection method based on visible light and voiceprint. The method comprises the following steps: respectively acquiring video images and environment sounds of old people in a monitoring environment through a visible light image module and an audio voiceprint acquisition module; a facial expression detection module is used for recognizing a human face in the video image, and a visual anomaly signal is output; recognizing an audio voiceprint signal in the environmental sound according to a voiceprint detection module, and outputting an audio abnormal signal; according to the method, the detection accuracy is effectively improved, the missing report is reduced, the method is suitable for home and old-age care institution environments, and the method is of great significance to guarantee the safety of old people and alleviate serious consequences caused by falling down.

The invention discloses a human body behavior prediction method and system, and the method comprises the steps: carrying out the skeleton point sequence extraction of a real-time behavior video image of a target person, and obtaining a joint point coordinate set and a skeleton motion sequence; determining a spatio-temporal feature sequence of the joint point coordinate set, and determining a skeleton motion sequence to perform spatio-temporal attention coding to obtain global spatio-temporal dynamic features; fusing the action probability distributions corresponding to the spatial-temporal feature sequence and the global spatial-temporal dynamic features to obtain short-time action probability distribution data; candidate action screening is carried out on the short-time action probability distribution data, and candidate action comprehensive features are obtained; performing feature coding on the historical action sequence of the target person to obtain a historical context vector, and splicing the historical context vector with the candidate action comprehensive features to obtain a fusion feature; and inputting the fusion features into a probability model for intention probability evaluation to obtain a behavior prediction result of the target person. According to the method, the accuracy of behavior prediction is improved.

The invention discloses a dynamic Gaussian digital human image rendering method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: determining a target multi-view image frame from a multi-view RGB video image sequence according to a preset attitude, and constructing a deformable parameterized model according to the target multi-view image frame; constructing an attitude space driving attitude corresponding to the multi-view RGB video image sequence based on a three-dimensional attitude estimation technology, and generating a two-dimensional position map according to the attitude space driving attitude and the deformable parameterized model; performing three-dimensional Gaussian binding on the deformable parameterized model to obtain a local attribute of the three-dimensional Gaussian; training is carried out according to the two-dimensional position map, and a target StyleUNet neural network is obtained; and predicting the new attitude through the target StyleUNet neural network to obtain a dynamic Gaussian digital human image. In this way, the high-fidelity drivable high-frequency detail digital human image can be automatically rendered.

The invention relates to the technical field of intelligent traffic, and discloses a camera state judgment method based on road surface covering and Hash comparison, and the method sequentially comprises the steps: extracting key frame pairs from a video stream at intervals; performing road surface region extraction on each frame of image to obtain a road surface mask; carrying out covering processing on the road surface area in each frame of image based on the mask to obtain a covered image; calculating a difference hash feature of each frame of covered image to obtain a hash code; calculating the Hamming distance between the Hash codes of the key frame pair; and judging whether the camera is in a rotating or stable state according to a comparison result of the Hamming distance and a threshold value. The method only depends on the video image data, does not need an external sensor, effectively eliminates the dynamic interference of the vehicle through covering the road surface, achieves the efficient and robust judgment of the state of the camera through combining the lightweight difference hash calculation, is low in calculation cost, and is suitable for edge equipment and complex road environments.

The invention discloses an unmanned aerial vehicle dynamic projection map construction method and system based on spatio-temporal data fusion, and belongs to the technical field of unmanned aerial vehicle video monitoring and geographic scene fusion, and the method comprises the steps: obtaining multi-modal flight data in real time based on an unmanned aerial vehicle, and the multi-modal flight data comprise video image data and spatio-temporal reference data; and performing data preprocessing on the multi-modal flight data to obtain route data. And constructing an unmanned aerial vehicle dynamic projection model, and performing space-time synchronous projection rendering on the route data to obtain an unmanned aerial vehicle dynamic projection map. And constructing a dynamic calibration model, and correcting the dynamic projection map of the unmanned aerial vehicle. According to the method, an integrated dynamic geographic information sensing system is constructed through deep cooperation of a spatio-temporal data fusion framework and an intelligent calculation engine, accurate mapping of the geographic space driven by spatio-temporal reference fusion is realized, millimeter-level space registration capability is constructed through multi-modal data intelligent solution and terrain adaptive rendering, and the real-time dynamic geographic information sensing system is constructed. And a technical breakthrough is formed in the dimensions of accurate positioning, data fusion, real-time response and the like.

The invention discloses an AIS information and video data fusion method. The method comprises the following steps: collecting and preprocessing AIS and video data; constructing a constant acceleration model to interpolate the AIS data, and projecting AIS latitude and longitude coordinates to a video pixel coordinate system to obtain a target trajectory; detecting a video ship by using a YOLOX model, and tracking through a MotionTrack algorithm to obtain a video trajectory; ship motion features of the tracks in the AIS and the video data are extracted respectively; constructing a comprehensive measurement function fusing the Euclidean distance, the course and the navigational speed similarity; performing data matching by adopting a Hungary algorithm to obtain a matching result of the AIS and the track in the video data; verifying a matching result through abnormal value elimination and category consistency verification; and according to a matching result, fusing and displaying the matched AIS information and the video image. The AIS information and the visual target information are comprehensively utilized, and the information matching result is determined by using multiple clues, so that the fusion accuracy is improved, and the precision and reliability of multi-source information fusion in maritime affair monitoring are improved.

The invention provides a signal lamp fault intelligent diagnosis method and system based on the Internet of Things, and relates to the technical field of data processing, and the method comprises the steps: 1, collecting the electrical parameters, environment data, working time sequence information and video image data of a signal lamp unit in real time, and constructing a multi-mode data set of the operation state of a signal lamp; step 2, transmitting the multi-modal data set to a central diagnosis platform, performing feature extraction and preliminary fault identification by using a pre-trained fault identification model, and generating fault type and fault position information; and step 3, based on the fault position information, selecting a plurality of monitoring devices in time-space association to form a diagnosis group, and performing fusion analysis and collaborative diagnosis on multi-source data of the diagnosis group to generate a fault judgment result. According to the invention, intelligent diagnosis and operation and maintenance management of the operation state of the signal lamp are realized, and the accuracy of fault identification and the processing efficiency are improved.

The invention belongs to the technical field of underground pipeline detection and defect identification, and particularly discloses an underground pipeline defect detection method based on image and point cloud data fusion, and the method comprises the following steps: collecting a video image and laser radar point cloud data of the inner wall of an underground pipeline, and carrying out the synchronous pairing of an image frame and a point cloud frame based on a timestamp; performing defect identification on the synchronized image frames to obtain a defect region ROI; based on a pre-calibrated external parameter matrix of a camera and a laser radar, converting the synchronously acquired point cloud data coordinates into an image coordinate system, calculating through internal parameters of the camera to obtain pixel coordinates, and screening out a point cloud point set falling into a defect region of interest (ROI) as a defect point cloud subset; and sequentially carrying out denoising, registration and clustering segmentation on the defect point cloud subset to obtain a three-dimensional point cloud structure of the defect, and calculating a quantization parameter of the defect based on the three-dimensional point cloud structure. According to the invention, automatic real-time detection and accurate quantification of pipeline defects can be effectively realized.

The invention relates to the technical field of video image intelligent analysis, in particular to a rockfall detection and early warning method and system based on videos. According to the method, a suspected rockfall target is recognized and judged by collecting a road or side slope monitoring video and combining target detection and motion trail analysis; and after the rockfall is judged, generating early warning information containing the position, time and danger level, storing the early warning information in a database, and triggering an early warning prompt of a user side at the same time. The system is composed of a video acquisition module, a detection and judgment module, an early warning generation module, a log storage module and a front-end interaction module, and all the modules achieve cooperation through data interfaces. Compared with the prior art, the method can still keep high detection accuracy and real-time performance in a complex environment, has the advantages of high automation degree and high expandability, can be widely applied to scenes such as highways, railways and mining areas, and remarkably improves the traffic and engineering safety level.

The invention relates to the technical field of video image processing, and discloses a video image super-resolution enhancement method based on a generative adversarial network. The method comprises the following steps: segmenting a low-resolution video sequence, and analyzing frame timestamp information to generate a dynamic time axis containing key frame nodes so as to reflect video time change characteristics; then, in combination with the time axis and an image feature extraction system, feature mapping is carried out on the low-resolution frames, and a preliminary high-resolution image sequence with feature information is obtained; enhancing the preliminary sequence by using a generative adversarial network containing a generator and a discriminator in combination with a context-aware optimization technology to generate an enhanced sequence; and a machine learning algorithm is introduced to identify and classify different image quality region characteristic modes, and an intelligent identification rule base is established, so that an enhanced sequence is optimized. And finally, in response to user interaction, adjusting display parameters to realize personalized display, integrating a multi-dimensional analysis tool, calculating details of a specific region, and presenting a result in a visual interface, so that the video image quality can be improved.

The invention relates to the technical field of endoscope video processing and intelligent edge computing, in particular to an endoscope video enhancement processing intelligent edge computing system. Comprising a data acquisition module which is used for acquiring an original video frame sequence of edge endoscope equipment in real time; the feature extraction module is used for determining an instantaneous feature vector representing the dynamic change of the operation scene; the criticality quantification module is used for quantizing and generating a surgical event criticality score; the tuning logic module is used for generating a discrete and stable calculation normal form switching instruction; the assembly line switching module is used for responding to the calculation normal form switching instruction and executing an asynchronous weight preheating strategy; the utility evaluation module is used for constructing a dynamic utility function to evaluate system performance; and the threshold value correction module is used for performing closed-loop correction on the high-criticality threshold value by adopting a gradient rising strategy. According to the method, the stability of system decision making is enhanced, the smooth transition of the video processing flow among different calculation paradigms is ensured, and the robustness of the system is improved.

The invention discloses a multi-model fusion target vehicle identification analysis method and system based on machine vision, and the method comprises the steps: A, capturing and collecting a road vehicle video / image through an unmanned plane, and transmitting the video / image to a streaming media server in a wireless manner; b, an improved target vehicle detection model is adopted, a multi-task cooperative detection framework is constructed, and vehicle illegal parking is judged by applying a multi-model fusion detection mechanism; c, designing a lightweight detection network, reducing the problems of high reasoning delay and large resource occupation in the multi-model cooperative detection process, and reducing the complexity of a detection algorithm; and D, carrying out intelligent alarm and event pushing. According to the invention, the method can improve the recognition accuracy of vehicle illegal parking behaviors in a complex scene, and improves the real-time performance and stability of a single-model monitoring system for vehicle target detection.