341 results about "Optical flow estimation" patented technology

The invention belongs to the technical field of river surface flow velocity measurement, and discloses a method and system for predicting river surface flow velocity based on improved optical flow estimation. The RAFT optical flow estimation model established by the invention comprises a query frame residual error estimation module and an optical flow residual error estimation module. The model continuously corrects the deviation between a prediction result and a true value by introducing an IRE mechanism so as to improve the estimation accuracy of a query frame residual estimation module and an optical flow residual estimation module, the query frame residual estimation module is used for performing deep optimization on feature processing and correlation calculation, and the optical flow residual estimation module is used for performing optical flow estimation. The method comprises the following steps: generating query frame estimation through a differentiable internal optimization process, and performing feature convolution operation on the query frame estimation and a query frame image so as to explicitly consider similar areas possibly appearing in a scene. An optical flow residual error estimation module is introduced into a CBAM module, the weight of a feature map is adjusted in a self-adaptive mode, complex water surface information is better captured, and the accuracy of optical flow iteration is improved.

The invention provides a rail transit video intelligent analysis method, medium and system, and belongs to the technical field of rail transit, and the method comprises the steps: constructing a self-adaptive resolution image hierarchical structure, and dynamically determining an optimal resolution hierarchy based on a minimum spanning tree algorithm; constructing an abnormal target movement matrix by combining an improved frame difference method with an optical flow estimation technology; applying a Hungary algorithm to realize target tracking; constructing an abnormal target change index to evaluate a behavior abnormal degree; dividing monitoring stages according to the running state of the vehicle to realize scene self-adaption; an orbit scene attention network model is introduced to identify abnormal behaviors; quantifying a risk level through an abnormal behavior risk assessment function; implementing a hierarchical processing strategy to enable a low-resolution hierarchy to be responsible for global rapid screening and a high-resolution hierarchy to be responsible for fine analysis of key areas; the risk trend is estimated in combination with the rail transit abnormal event prediction model, and the technical problems of large calculation amount and low efficiency of high-definition video processing are effectively solved.

The invention discloses an efficient optical flow estimation method and device based on Mama, and the method comprises the steps: carrying out the normalization and size alignment of two adjacent frames of images, and extracting the dense features of a fixed down-sampling rate through a shared weight convolution encoder; the two-frame features are sent to a multi-level feature enhancement module, an intra-frame modeling unit and a cross-frame interaction unit are cascaded and matched with channel reforming and residual error correction, and enhanced features are obtained; constructing a four-dimensional cost body on a low resolution, performing probability normalization along a target coordinate dimension, weighting a target coordinate grid according to a probability to obtain a corresponding coordinate, and subtracting the corresponding coordinate from a source coordinate to obtain an initial optical flow; and carrying out attention-guided space fusion on the initial optical flow and context and local correlation, sending the fused optical flow to a differential Mama-based autoregressive refinement module, carrying out iterative updating according to a small number of fixed steps, recovering to a target resolution through convex combination up-sampling, and outputting a final optical flow. According to the method, the optical flow field can be accurately estimated under the conditions of low complexity and low time delay.

The invention discloses a two-stage infrared and visible light image registration method, system and device. The method comprises the following steps of image preprocessing, contour extraction, angular point detection, feature description and matching, affine transformation estimation, multi-scale optical flow estimation, optical flow constraint and loss function, reverse resampling and fusion and error evaluation. According to the invention, rough registration is carried out by using contour angular point features, so that a preliminary alignment result can be quickly obtained; refined alignment is carried out in combination with an unsupervised optical flow network, and sub-pixel-level registration precision is achieved. The contour angular points are based on shape information of an image target, are natural and are not influenced by spectral differences, and the matching stability is enhanced through main direction and angle features. The unsupervised depth optical flow model estimates a pixel displacement field by learning consistency characteristics of an input image, and does not need to depend on annotation data. The combination can effectively eliminate the difference between infrared light and visible light, and improves the robustness and adaptability of registration.

A slope monitoring method based on optical flow estimation and binocular vision belongs to the technical field of deformation monitoring and measurement, and adopts the technical scheme that a binocular stereoscopic vision system is built, and camera acquisition parameters are calibrated; transmitting the image to a server in real time through the Internet of Things; distortion correction and stereo matching are carried out to obtain a depth map; monitoring points are manually or automatically arranged on the corrected image, and dense optical flow estimation tracking pixel point movement is carried out; world coordinates of observation points and movement conditions of each frame are obtained, and displacement changes of slope monitoring points are obtained by combining coordinates before and after displacement; and obtaining a displacement rate by combining monitoring time and displacement, judging slope safety, obtaining a displacement track, and performing linear interpolation to obtain a slope deformation cloud picture, thereby realizing the monitoring purpose. The slope displacement monitoring method has the advantages that slope monitoring can be carried out more flexibly and efficiently, the safety of workers is improved, the slope monitoring cost is reduced, real-time slope displacement monitoring is achieved, and the intensive degree of slope monitoring points is greatly improved.

The invention discloses a water area detection method based on multi-modal fusion perception, so as to improve the accuracy and real-time performance of urban waterlogging monitoring. The method comprises the following steps: combined feature extraction of optical flow and visual attributes: collecting continuous image frames, and extracting visual and motion features by using an optical flow estimation network and a convolutional neural network (CNN) in a combined manner to realize accurate perception of water area changes; feature fusion driven by a space-time attention mechanism: constructing the space-time attention mechanism, fusing multi-level features, and forming unified space-time feature representation so as to enhance the detection capability of the ponding area; constructing an end-to-end waterlogging detection model: designing a deep learning network fusing classification and regression tasks, and realizing accurate identification and positioning of a waterlogging ponding area; real-time ponding range tracking and change analysis: based on a dynamic monitoring technology, analyzing an expansion trend of a ponding area, and providing accurate early warning information to support emergency decision making; according to the method, static visual features and dynamic motion features of a water body are extracted in parallel, a multi-modal feature fusion strategy is adopted, and an end-to-end intelligent detection model is constructed. Experimental verification shows that the method has high detection precision and real-time performance in a complex electromagnetic environment, the reliability and intelligent early warning capability of urban waterlogging monitoring are remarkably improved, and powerful technical support is provided for urban water area management and disaster prevention and control.

The invention provides a traffic scene multi-target detection method and system based on deep learning, and relates to the technical field of traffic, and the method comprises the steps: carrying out the semantic prior driven multi-scale feature extraction of a multi-frame image, and carrying out the point-by-point fusion; obtaining a motion field through optical flow estimation and feature similarity calculation, and executing motion compensation to obtain a moving target mask; obtaining a static target boundary by using boundary regression decoupling and geometric consistency constraint; and finally, moving and static target results are combined, and quadratic regression is executed based on consistency evaluation. The dynamic and static targets in the traffic scene can be effectively detected, the boundary regression precision is improved, and the false detection rate caused by shielding is reduced.

The invention relates to an unmanned aerial vehicle dynamic environment sensing method based on a visual large model, electronic equipment and a medium, and the method comprises the steps: carrying out the instance segmentation and optical flow estimation of multiple frames of original images based on the visual large model and an optical flow estimation network, and obtaining a dynamic mask and inter-frame optical flow information; performing feature point classification on the depth information, the dynamic masks and the inter-frame optical flow information corresponding to the multiple frames of images to obtain a classification result; constructing a re-projection error model based on the classification result to obtain an estimated pose; evaluating the quality of the feature points based on the estimated pose to obtain an optimized pose; and constructing a feature point global map based on the world coordinates of the continuous frame road sign points and the optimized poses. Through a multi-information fusion and step-by-step optimization mechanism, the method has relatively high robustness, can adapt to various dynamic environments, and improves environment perception performance and operation reliability in different scenes.

The invention discloses a vehicle monitoring method based on frame difference and deep learning fusion, and relates to the technical field of vehicle monitoring, cameras and environment sensors are deployed in a monitoring area, and videos and multi-source data are acquired by means of vehicle-road cooperation; a self-adaptive frame difference method is used, morphology and optical flow estimation are matched, a threshold value is determined according to the environment, and vehicle features are extracted; constructing a deep convolutional neural network with an attention mechanism, and training a model by using various data in combination with migration and reinforcement learning; fusing the two types of features based on a graph attention network to form high-quality fusion features; a space-time diagram convolutional network is combined with an LSTM to track a vehicle and predict a trajectory, a behavior pattern library is constructed to judge abnormity, and classification analysis is performed in combination with an SVM and a knowledge graph. According to the invention, the frame difference and deep learning are fused, the monitoring accuracy is improved, and the vehicle can be accurately identified and detected; the real-time performance is enhanced, the data is quickly processed, and the environmental influence is reduced; traffic management is assisted, and a safe and efficient traffic environment is created.

The invention discloses a river surface flow velocity measurement method and system based on physical constraint and multi-modal fusion, and relates to the technical field of multi-modal data processing, a target area is determined, and river surface image sequence data, depth information and environment data of the target area are acquired; preprocessing the collected data, embedding redundant codes in data representation, and synthesizing to form a basic data set; and based on the basic data set, performing preliminary optical flow estimation on the river surface image sequence data by using an optical flow method. According to the method, optical flow information, depth information and environment data are fused, comprehensive utilization of multi-source data is achieved, in the data fusion process, redundant codes in data representation are combined for error detection and correction, error points in the data are effectively recognized and corrected, the reliability and accuracy of the data are improved, and the data fusion efficiency is improved. And by utilizing a multi-modal data fusion and error correction mechanism, the robustness of the system is remarkably enhanced, so that the system can stably operate in a complex and changeable river environment.

The invention belongs to the technical field of computer vision and deep learning, and particularly relates to an optical flow estimation method and system fusing Mama and visual basic model knowledge. The method comprises the following steps: performing down-sampling feature extraction on two adjacent frames of input images by using a convolutional neural network to obtain local texture features; performing down-sampling on the first frame image to obtain context features; meanwhile, extracting global semantic features of two adjacent frames of images by using a pre-trained visual model, and performing adaptive fusion enhancement through an adaptive semantic texture feature fusion module to obtain an image coding feature pair after semantic enhancement; constructing a related volume through pixel-by-pixel dot product operation; and finally, based on the obtained related volume and context features, iteratively optimizing the output optical flow through a loop iteration updating module. The method solves the problems that in a low-texture, repeated-texture or sheltered area, feature expression is unstable, self-adaptive modeling capacity is lacked, different scenes are difficult to generalize, and model performance and efficiency cannot be balanced.

The invention relates to the technical field of remote sensing image change detection, in particular to a remote sensing image change recognition method and system fusing time sequence alignment and semantic perception, and the method comprises the steps: obtaining remote sensing images of different time phases in the same region, carrying out the multi-scale feature extraction, and achieving the feature alignment through optical flow estimation under the same scale, performing weighted fusion in combination with an attention mechanism to obtain a fused multi-scale feature group; further through multi-scale convolution and channel and spatial attention enhancement context and detail expression, generating a high-resolution feature map, and finally outputting a pixel-level change recognition map for indicating whether a corresponding geographic position is changed or not; by means of the synergistic effect of optical flow estimation and an attention mechanism, false changes caused by geometric displacement, shadow drifting and seasonal spectral difference can be effectively inhibited, and the stability and reliability of a detection result are improved; and meanwhile, under the support of multi-scale convolution and attention weighting, the recognition capability of the small target and the boundary region is enhanced.

Techniques are described for optical flow estimation. For example, a computing device can obtain images including at least a first image and a second image. The computing device can process the first image and the second image using a first neural network to obtain a set of features representing the first image and the second image. The computing device can predict, based on the set of features, a latent representation of a change in an optical flow between at least the first image and the second image using a neural ordinary differential equation that uses a second neural network to generate a predicted latent representation. The computing device can estimate the optical flow based on the predicted latent representation to generate an estimated optical flow, wherein the optical flow is associated with movement of pixels from at least the first image to the second image.

The invention discloses a heart motion feature extraction method based on optical flow estimation, and relates to medical image processing. Preprocessing the input four-dimensional space-time cardiac magnetic resonance imaging data, namely, scaling pixel values; inputting two frames of images which are continuous in time into a feature encoder and a context encoder for feature extraction, wherein the two frames of images are divided into a reference frame and a moving frame; calculating the correlation between the feature maps through a correlation volume calculation module, constructing a correlation pyramid, and extracting the feature maps to provide matching information for subsequent optical flow estimation; the motion feature iteration enhancement module iteratively and continuously refines an optical flow estimation result through a deformable convolution and global motion aggregation (GMA) module; model parameters are optimized based on a weighted sum of luminosity consistency loss, smoothness loss, and gradient consistency loss. Edge features are adaptively captured through deformable convolution, global and local features are fused by using a GMA module, optical flow prediction errors are effectively reduced, motion estimation quality is improved, and key features of a heart edge region are maintained.

The invention relates to the technical field of computer vision, in particular to a space-time consistency data generation method for visual target tracking. The method comprises the following steps: firstly, training a path generator on a target tracking training set, learning a motion law of a target in a time sequence by using optical flow estimation and conditional variation coding technologies, and generating a target motion track conforming to physical constraints; and then, based on the generated target trajectory, introducing a space-time consistency attention mechanism to guide a text-video generation model, and under the condition of keeping basic model parameter freezing, constraining the position, scale and continuity of a target in a generation frame through an attention network, thereby synthesizing a video frame sequence with real motion features. According to the method, target tracking video data with real motion characteristics and high time sequence consistency is generated, and the robustness of the model to complex motion, illumination change and shielding conditions can be improved in different scenes.

The invention discloses a power grid intelligent inspection method and system based on an unmanned aerial vehicle, and the method comprises the following steps: collecting image flow data of a target region, constructing a three-dimensional point cloud model, and generating an initial inspection path based on a fast marching tree method in combination with spatial position information; controlling the unmanned aerial vehicle to fly according to a path and collect image frames in real time, and executing an optical flow estimation algorithm through an edge calculation chip to obtain a pixel motion vector; associating the motion vector with a space coordinate corresponding to each inspection point in the inspection path, dividing an optical flow detection area and distributing an initial detection weight; recognizing a dynamic abnormal area according to the motion features, extracting image data and space coordinates, and driving an acousto-optic load assembly carried by the unmanned aerial vehicle to respond; and based on the space coordinates of the abnormal region, re-executing the fast marching tree method to generate a local update path, and adjusting the detection weight of the related region. According to the invention, dynamic sensing and path updating linkage in the unmanned aerial vehicle inspection process can be realized.

The invention discloses a semantic SLAM method combining instance segmentation and optical flow estimation, and belongs to the technical field of robot positioning and navigation.The method comprises the steps that a dynamic region detection thread, a semantic map building thread and a point-line feature fusion method are fused into an ORB-SLAM 3 system; the dynamic object detection thread is combined with an improved YOLOv8-seg semantic segmentation network and an optical flow estimation network RAFT, images are collected through an RGBD camera, point feature extraction and line feature extraction are carried out on the obtained images, the input images are segmented through the semantic segmentation network, masks of objects are obtained, area masks are obtained through the optical flow estimation network, and the dynamic object detection thread is obtained. And combining semantic segmentation and optical flow estimation masks to determine a final dynamic region, and finally removing point features and line features in the dynamic region. According to the method, the precision and robustness of the SLAM system in a dynamic environment and a weak texture environment are improved by combining semantic information and optical flow information to remove dynamic objects and fusing point and line features, and the method can be applied to real-time semantic map construction in a dynamic scene.

The invention discloses a lightweight optical flow neural network method for river velocity measurement, and the method comprises the steps: collecting a dynamic monitoring image of a water area surface flow field, calculating an approximate optical flow field through combining with a DeepFlow optical flow algorithm, and generating an optical flow label; an improved lightweight ConvFFN-Flow optical flow estimation model is built, and the model comprises a feature extraction module, a 4D related structure body module, a GRU optical flow residual error iteration module and an up-sampling module; by introducing a ConvFFN module, the model parameter scale and the calculation amount are reduced, and the optical flow prediction precision is kept; and based on pixel coordinates of front and rear frames of images of the optical flow field data, obtaining corresponding positions in a real three-dimensional space, calculating an actual displacement amount, and deducing to obtain a real flow velocity vector size and a flow direction of the water body surface. The method still has stable and high-precision optical flow estimation capability when being used for processing scenes such as fine river water surface texture and complex flow state change, and meanwhile, the calculation overhead and the storage demand are remarkably reduced.

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 moving target extraction method and device for event camera static background blanking, and relates to the technical field of computer vision, an event camera is arranged on a motion scanning platform, and the detection range is expanded; the method comprises the following steps: acquiring original event data from an event camera, and dividing the original event data into event data packets according to a time window; estimating static background event light flow in each time window by adopting a pre-constructed background event global light flow estimation model; restoring the original events of the event data packets of the continuous time windows to the same reference timestamp by using the static background event light flow, and constructing a corresponding restored event data set; and extracting a dynamic target by adopting a gradient-based moving target separation algorithm in combination with the reduced event data set. According to the invention, the event camera can realize the static background blanking capability on the motion scanning platform, the independent display of the moving target is recovered, and the technical advantages of the event camera in the remote sensing detection of the moving target are fully exerted.

The invention discloses a dual-light imaging automatic registration method of a near-infrared and visible light shared sensor, relates to the field of dual-light imaging image registration, and aims to solve the problem of difficult dual-light camera image registration in the prior art. An infrared cut-off filter and a single near-infrared pass filter alternately pass through a lens through a filter circulation switching mechanism, an image formed by interweaving a visible light image and a near-infrared image is shot, optical flow calculation is carried out through the image which is well imaged, pixel motion of an image frame with low imaging quality is estimated, and the image quality is improved. And finally, registering and outputting the high-imaging-quality image and the estimated image. According to the method, accurate matching of the visible light image and the near-infrared image in the aspect of spatial parameters is achieved, meanwhile, a mapping transformation matrix with complex calculation is converted into simple optical flow estimation, rapid, efficient and accurate registration of the visible light image and the near-infrared image is achieved, and therefore a registration image with clear pictures and unified spatial parameters is obtained.

The invention provides an event camera optical flow estimation method based on motion perception and space-time collaborative modeling. The event camera optical flow estimation method comprises the following steps: preparing a data set; dividing and coding the event data into space-time voxel representation; constructing a motion perception space-time aggregation model, wherein the motion perception space-time aggregation model comprises a bidirectional correlation volume module, a spatial feature enhancement module and a space-time motion collaboration module; and training the model by using the training set, continuously optimizing model parameters through a back propagation algorithm, and generating a corresponding high-resolution dense optical flow graph. According to the method, the problem of spatial information loss caused by event data sparsity can be effectively relieved, and the stable perception capability of a motion track is enhanced by using time consistency, so that the accuracy of optical flow estimation is remarkably improved; the method is suitable for task scenes with high requirements on accurate motion perception and real-time response capability in high-dynamic environments such as automatic driving, robot navigation and intelligent monitoring.

The invention relates to the technical field of target tracking, and discloses a multi-target smear-free tracking method and device in a high-dynamic scene, and the method comprises the steps: obtaining a continuous frame image sequence of a camera in the high-dynamic scene, and extracting a multi-target speed variation and a bounding box scale change sequence; performing global and local feature discrimination through a double-discriminator network to obtain a global discrimination feature vector and a local discrimination feature vector; self-adaptive scale compensation parameters are calculated, multi-source scale fusion is carried out, and a multi-scale smear removal prediction template is obtained; and carrying out light stream estimation smear elimination based on the multi-scale smear removal prediction template to obtain a first multi-target tracking result, carrying out scale consistency identity association on the first multi-target tracking result, and outputting a second multi-target tracking result. The problems of identity switching and shielding in a high dynamic scene can be effectively solved.

The invention provides a river surface flow velocity estimation method and device fusing multi-mode optical flow estimation and PINN, and relates to the technical field of hydrological monitoring, and the method comprises the steps: obtaining meteorological observation data and continuous multi-frame river surface images; performing multi-scale feature processing on the plurality of frames of river surface images to obtain a plurality of related pyramid feature data; carrying out coding processing on the meteorological observation data and at least one frame of image in the continuous multiple frames of river surface images to obtain context mixed feature data; and obtaining an optical flow field according to the multiple pieces of related pyramid feature data and the context mixed feature data, and correcting the optical flow field to obtain a target optical flow field. According to the scheme, high-precision prediction of the river surface flow velocity is realized.

The invention discloses a bionic visual positioning system and method based on multispectral perception and a storage medium, and the system comprises a front-end multidimensional optical perception module, a mesopic vision preprocessing module and a rear-end fusion perception and target positioning module, the front-end multi-dimensional optical sensing module is used for performing optical imaging, spectral decomposition and polarization imaging; the mesopic vision preprocessing module is used for carrying out image preprocessing and optical flow estimation; and the rear-end fusion perception and target positioning module is used for feature fusion and target identification and positioning. According to the bionic visual positioning mechanism, through a bionic compound eye structure + multispectral + polarization + optical flow fusion perception strategy, the perception bottleneck of a traditional visual system in a high-dynamic and complex illumination environment is effectively solved, and the bionic visual positioning mechanism has the advantages of high robustness, low delay, wide field of view, multiple modes and the like; the method is especially suitable for application scenes such as automatic driving, unmanned aerial vehicles, robots and the like with extremely high requirements on real-time performance and reliability.

The invention discloses a nut looseness detection method and device based on a mask guided segmentation network and a medium, and the method comprises the steps: image collection and diversified data enhancement; a step of enhancing a multi-path segmentation network by a mask guide boundary; a step of annular and segmented division based on the fine segmentation mask; a step of constructing an optical flow estimation model; a step of obtaining a displacement time sequence and a time sequence loosening characteristic; and judging the loosening state of the nut based on the self-adaptive threshold value. Compared with the prior art, the nut looseness detection method based on the mask guided segmentation network has the advantages that the definition of the segmentation boundary and the sensing capacity of small-amplitude rotation / radial displacement are remarkably improved under the background of strong reflection and noise, short-time random disturbance and continuous looseness can be distinguished, physical marks are not needed, and the detection accuracy is high. The method is suitable for in-service monitoring of wind power equipment and visual state detection of other fastener structures.

The invention relates to the technical field of image processing, can be applied to the fields of old-age care and the like, and discloses an unsupervised abnormal behavior detection method, device and equipment and a storage medium, and the method comprises the steps: collecting video data, and carrying out the frame extraction processing of the video data, and obtaining a previous frame of a current frame, the current frame and a next frame of the current frame; inputting a previous frame of the current frame and a next frame of the current frame into an optical flow estimation network and an interpolation network to generate a prediction frame; inputting the current frame and the prediction frame into a feature extraction network to respectively obtain a current feature and a prediction feature; calculating a loss function value according to the current frame, the prediction frame, the current feature and the prediction feature; judging whether the loss function value reaches a preset threshold value; and if the loss function value does not reach the preset threshold value, determining that the behavior is abnormal. According to the invention, through the optical flow estimation network and the interpolation network, whether others show abnormal motion trails can be effectively identified, so that the accuracy and robustness of abnormal behavior detection are improved.

The invention discloses an end-to-end monocular visual odometer method for adaptively adjusting a region of interest, and the method comprises the steps: constructing a compact connection architecture of an optical flow estimation network and a pose estimation network, and carrying out the seamless integration of optical flow prediction, feature extraction, attention optimization and pose regression based on end-to-end deep learning design. Meanwhile, a double attention mechanism (CBAM module) and semantic feature fusion are introduced, interference of dynamic objects, low textures and fuzzy regions is filtered, the semantic feature fusion is combined, the understanding ability of the model to a scene is improved, the pose estimation precision and the network structure are optimized, the lightweight CBAM module and efficient residual block design is adopted, the calculation amount is reduced, and the algorithm is more accurate. And adaptive adjustment of feature extraction and scene understanding enhancement are realized. The complexity of the system is reduced, manual design and adjustment and optimization of a plurality of modules are not needed, error accumulation among the modules is avoided through end-to-end design, the overall performance is more stable, and efficient pose estimation is achieved on embedded equipment with limited resources.

The invention discloses a road disaster identification method based on multi-modal feature fusion, and relates to the technical field of road safety monitoring, and the method comprises the steps: extracting a space-time optical flow field through a lightweight PWC-Net optical flow estimation algorithm based on an unmanned plane video frame sequence in a multi-modal data stream of space-time alignment, and generating space-time optical flow field data; acquiring disaster equivalent current density according to the space-time optical flow field data and millimeter wave radar dielectric constants in the multi-modal data flow, separating disaster physical components through a gating mechanism, and generating a decoupled disaster physical field; on the basis of the decoupled disaster physical field and in combination with the space-time aligned multi-modal data stream, constructing a space-time feature tensor, and performing multi-modal fusion through a lightweight graph convolutional network to generate a road disaster early warning report; according to the invention, the time deviation is calculated and corrected by using the quantum state correlation degree formula, accurate alignment of multi-modal data is realized, the fusion consistency is improved, and the accuracy and robustness of disaster identification are enhanced.

Embodiments of the present application disclose a video frame interpolation method. The method comprises: acquiring a plurality of consecutive frames from a video file to be processed, the plurality of consecutive frames comprising a target frame and the frame preceding the target frame and the frame following the target frame; on the basis of an optical flow estimation model, obtaining a reverse optical flow map between the target frame and the preceding frame and a forward optical flow map between the target frame and the following frame; on the basis of the similarity between the forward optical flow map and the reverse optical flow map, determining whether a transition exists among the plurality of consecutive frames; and, when no transition exists among the plurality of consecutive frames, interpolating a frame between the target frame and the following frame by means of an optical flow frame interpolation model. The embodiments of the present application further disclose a video frame interpolation system, a computer device, and a computer storage medium. The technical solution provided by the embodiments of the present application can prevent the appearance of artifacts in a synthesized intermediate frame due to the use of an unsuitable optical flow frame interpolation model for frame interpolation when a transition exists, while also avoiding misjudgment of a transition scene.