11 results about "Aerial video" patented technology

This invention belongs to the field of UAV autonomous positioning and navigation technology, and relates to a UAV map construction method for environments where satellite imagery is unavailable. Specifically, it provides a method for constructing a UAV work area map based on aerial imagery, addressing issues such as image distortion and edge misalignment in existing area map construction methods. This invention orthogonally divides the UAV's aerial photography work area into a grid according to the same geographic coordinate system. The UAV performs a coverage flight mission within the work area according to a planned flight path, maintaining an interval of 50% of the field of view width between adjacent flight paths. During aerial photography, the UAV simultaneously acquires image and geographic positioning information (GPS information and yaw angle information of the center point) as image location points. Based on UAV sensor data and the UAV's flight trajectory and pose, keyframes of geographic location information are extracted from the aerial video, and then used to fill the corresponding grid content to obtain a global map of the work area.

This invention provides an intelligent monitoring and boundary violation early warning method for construction planning red lines based on UAV aerial photography, belonging to the field of construction engineering management. The method utilizes a UAV equipped with a high-resolution camera and an RTK system to plan a full-coverage flight path to collect aerial video, location information, and attitude parameters of the construction area, extracting keyframes and correlating the data. After sample construction and data augmentation, a semantic segmentation model is trained, and a binary mask of the construction area is generated by combining morphological operations. The coordinates of feature points on the planning red line CAD drawing are analyzed, and a red line mask is generated through a three-level transformation. The two masks are compared, and the boundary violation area is extracted. The proportion of the boundary violation area and the maximum boundary violation distance are calculated. Alarms are triggered in stages according to preset thresholds, and a monitoring report is output. This invention constructs an integrated automated monitoring system, solving the shortcomings of traditional manual inspections, such as low coverage, poor real-time performance, and high misjudgment rate, significantly improving the accuracy and efficiency of construction red line monitoring and providing strong support for compliance management.

The application discloses a kind of aerial video real-time labeling method and device.The application includes: after unmanned aerial vehicle takes off, the position of unmanned aerial vehicle is obtained to obtain the fence of unmanned aerial vehicle, and the fence of unmanned aerial vehicle is stored in fence library, wherein the range of the fence of unmanned aerial vehicle is circular area with the longitude and latitude point of unmanned aerial vehicle as center, and the radius of circular area is greater than 0 Real number;All point positions in the camera coordinate system of unmanned aerial vehicle are obtained, the point positions satisfying the equation of frustum are retained, and the point positions not satisfying the equation of frustum are removed;Point position is aggregated according to the height of unmanned aerial vehicle.Through the application, the problems of low efficiency, resource waste and inaccurate point scattering in related art of unmanned aerial vehicle real-time loading are solved.

The application discloses an aerial target detection method and device based on an attention mechanism, and a medium, relates to the technical field of image detection, and comprises the following steps: extracting a center coordinate sequence from a high-trust candidate frame, performing density aggregation to obtain an aggregation area frame, and performing boundary expansion, center padding and scale zooming to generate an aggregation cropped frame; constructing multi-scale features according to the aggregation cropped frame and a corresponding aerial video frame, and combining a candidate target pixel scale interval to perform scale prior-driven attention routing arrangement, to generate cross-flow gating weights and attention features; performing feature fusion on the aggregation area frame and the cross-flow gating weights through the attention features to obtain a real-time detection frame, and performing overlap conflict resolution and non-maximum suppression to generate an aerial target detection result set. The application realizes cross-flow gating feature fusion, is used for enhancing the image detection sensitivity of the aggregation area, reducing the missed detection rate and inhibiting the false merging of dense targets.

This invention discloses a method and system for color recognition and classification statistics of safety helmets used in water conservancy engineering construction based on unmanned aerial vehicles (UAVs). The method includes: acquiring aerial video data of a water conservancy engineering construction site taken by a UAV along a preset flight path; performing defogging and illumination compensation on the acquired video frames, and segmenting the original image into multiple overlapping sub-images; inputting the sub-images into an improved YOLOv8 model for safety helmet detection and color classification; tracking the detected targets using the ByteTrack algorithm, assigning a unique ID to each target, and introducing a global motion compensation module during the tracking process to offset the influence of the UAV's own motion; performing identity verification based on a temporal buffer and classification and counting based on spatial logic; mapping the safety helmet color to a preset job type, and outputting the number of workers on duty for each job type and / or early warning information. This invention enables efficient and refined management in complex environments during water conservancy engineering construction.

This invention relates to the field of cellular-connected drone video transmission technology, specifically disclosing a method for maximizing user experience quality in multi-drone aerial video transmission. This method is used in aerial video streaming systems that utilize multiple cellular-connected drones to capture video from different points of interest (PoI) regions and transmit the video to ground base stations (BS) and users, allowing ground users to share the drones' field of view. Based on this aerial video streaming system, this invention constructs an optimization problem. By jointly optimizing transmission scheduling, video playback rate, and drone trajectory design, it maximizes the minimum quality of experience (QoE) for all users, considering uplink interference and the trade-off between video quality and smoothness. Simulation results show that, compared to benchmark solutions, the proposed solution achieves a significant performance improvement and achieves a trade-off between video quality and smoothness.

The application relates to the technical field of unmanned aerial vehicle target detection, in particular to an unmanned aerial vehicle video target detection method and system based on differential perception. The method comprises the following steps: acquiring an unmanned aerial vehicle aerial video frame sequence to be detected, inputting the unmanned aerial vehicle aerial video frame sequence to a preset target detection model, and outputting target detection results of each frame; the target detection model performs multi-scale feature extraction on each frame to obtain frame features; the frame features are input into a differential perception Transformer coding module to output differential features and coding features; the differential features, the coding features and an initialized target query seed are jointly input into a differential perception diffusion module to generate a target query aligned with the current frame; and the target query and the coding features are subjected to feature decoding and target matching through a decoder to obtain the target detection results of each frame. The application can suppress pseudo-motion interference, adapt to dynamic changes in target density, solve the problem of unmatched query targets, and improve the precision and robustness of unmanned aerial vehicle video target detection in complex scenes.

The present application relates to the technical field of unmanned aerial vehicle, especially to a kind of unmanned aerial vehicle aerial target detection repair method for optical distortion, comprising the following steps: obtaining unmanned aerial vehicle aerial video sequence, extract continuous frame image;Based on the statistical characteristics of thermal turbulence in a short time, construct space-time varying distortion model, distortion estimation is carried out to video sequence.The present application can effectively estimate and reverse optical distortion caused by thermal turbulence by constructing space-time varying distortion model and combining blind deconvolution technology, thereby significantly improving the definition and geometric consistency of unmanned aerial vehicle aerial image, this method makes full use of the time sequence information between continuous frames and turbulence statistical characteristics, realizes the accurate modeling of distortion field, and then restores the image with more detailed and clearer edges by inverse processing, provides high-quality input for subsequent target detection.

The application discloses a kind of based on spatiotemporal feature fusion's aerial track foreign matter invasion detection method and system.Method is through unmanned aerial vehicle collection continuous aerial video stream and pre-processing;Spatiotemporal feature fusion detection network based on improved YOLOv11 is constructed, and track foreign matter invasion detection is converted into the double target detection task of normal track area and abnormal track area.Fused-ConvGRU module is introduced in Neck module, and the spatiotemporal feature of continuous multiple frame feature maps is refined and propagated;Depth separable convolution and channel pruning strategy are used to realize model light weight.Model is trained using dataset, and real-time video stream is detected frame by frame, and alarm is generated when the confidence of abnormal track area exceeds the preset threshold value.The application does not need to preset track area and foreign matter category, can adapt to aerial view change, detection precision is high, generalization ability is strong, and light weight design is suitable for unmanned aerial vehicle embedded platform real-time deployment.

The service provides tourists visiting tourist spots who can simply take a picture of a flight request board with their data terminal, and then view video files of aerial footage captured by automated flight on the data terminal they operate. [Solution] When a tourist operates one of the first to fourth data terminals, and the camera captures an image of a flight request instruction board placed at a tourist spot, the connection unit connects to the aerial photography platform 1A provided by the server device 1. The server device 1 sends a flight start signal to the unmanned aircraft (drones) D1 to D4 to take aerial photographs of the tourist spot. While flying along the stored aerial photography flight route, the cameras CAM1 to CAM4 capture aerial video data, which is then sent to the server device 1. The server device 1 then uses AI editing to edit the received aerial video data and sends it back to one of the first to fourth data terminals operated by the tourist.

This invention belongs to the field of intelligent transportation and autonomous driving technology, and discloses a vehicle trajectory prediction method and system based on video data fusion and spatiotemporal features. The method includes: acquiring drone aerial video streams; generating vehicle temporal trajectories using target detection and tracking algorithms; converting pixel coordinates to real-world coordinates using a size prior mapping method; constructing historical trajectory sequences and performing normalization and standardization processing; generating temporally enhanced feature vectors using a long short-term memory network and attention mechanism; dynamically filtering neighboring vehicles and generating spatial interaction feature vectors through a social pooling interaction module; inputting the fused spatiotemporal features into a trajectory prediction decoder to generate a future trajectory prediction sequence; and denormalizing the prediction sequence and mapping it to the video image coordinate system for visualization output. This invention achieves end-to-end integrated perception and prediction, significantly improving the accuracy, stability, and real-time performance of vehicle trajectory prediction in complex driving scenarios.