447 results about "Vision algorithms" patented technology

The invention provides a reconstruction method of a three-dimensional reconstruction model based on two-dimensional Gaussian splashing, which comprises the following steps: S1, carrying out sparse reconstruction on an input image sequence through a multi-view stereoscopic vision algorithm to generate an initial sparse three-dimensional point cloud and a corresponding camera pose parameter; s2, inputting an improved two-dimensional Gaussian radiation field by using the sparse three-dimensional point cloud and the camera pose as information; s3, dynamically screening a visible anchor point subset based on the current view angle parameter, and generating a rendered image through a differentiable rendering pipeline; s4, calculating a loss function of the rendering image of the training track and the input image to optimize a reconstruction scene; and S5, starting a special visualization tool, and inputting a rendering result. According to the method, by introducing a trimmable anchor point parameterization framework and a multi-scale feature fusion mechanism, light-weight and high-precision three-dimensional scene modeling is achieved, and the problems that traditional 2D Gaussian sputtering is insufficient in multi-view geometric consistency, storage overhead and weak texture region reconstruction and an existing 2D Gaussian splashing method is insufficient in self-adaptive mechanism are solved.

A control method and system for precise landing of an unmanned aerial vehicle. The method comprises the following steps: on the basis of a multi-sensor fusion technique, using a lidar and a stereo vision algorithm to collect environmental data and perform primary processing, and generating comprehensive environmental perception data (step S1); on the basis of the comprehensive environmental perception data, using a digital elevation model algorithm to re-construct a three-dimensional terrain of a landing area, and generating a three-dimensional terrain model (step S2); on the basis of the three-dimensional terrain model, using an A* search path-planning algorithm to perform risk assessment and plan a safe landing path, and generating an optimized landing path (step S3); on the basis of the optimized landing path, using an adaptive control algorithm to adjust a flight attitude in real time by means of a fuzzy logic controller, and generating flight parameter adjustment (step S4); on the basis of the flight parameter adjustment, using machine vision and a decision tree algorithm to execute autonomous obstacle avoidance and emergency response, and generating a safe landing execution scheme (step S5); and on the basis of the safe landing execution scheme, using an ultrasonic sensor and a ground feedback system to confirm and fine-adjust a landing point, and completing final landing confirmation (step S6).

The invention discloses a dynamic environment dense point cloud SLAM (Simultaneous Localization and Mapping) method and system based on YOLOv11 and ORB-SLAM 3. According to the method, real-time target detection and image segmentation technologies of a YOLOv11 model are integrated into an ORB-SLAM 3 framework, so that high-precision and robust visual SLAM in a dynamic environment is realized. A traditional convolution layer is replaced by using a balanced convolution method GSConv layer in a YOLOv11 model, and a traditional C2f layer is replaced by using a new feature fusion module VoVGSCS layer, so that a Neck structure of the YOLOv11 is improved, and a lightweight network model is realized. Experimental data prove that the pose estimation precision of the method in a dynamic environment is remarkably superior to that of an existing visual SLAM algorithm.

The invention relates to a cloth color difference intelligent detection method based on an improved vision algorithm, and the method comprises the following steps: S1, obtaining the spectral image data of cloth, and carrying out the calibration; s2, setting of a high-definition camera is adjusted according to the real-time collected image features, and high-definition image data of the cloth is obtained based on the high-definition camera; s3, preprocessing the high-definition image data; s4, based on the preprocessed high-definition image data, performing cloth and background segmentation by using a deep network EfficentNet in combination with an Attention mechanism, extracting features, and establishing a comprehensive feature vector; s5, constructing a color difference detection model based on the improved SVM, and carrying out color difference detection on the cloth image based on the comprehensive feature vector; and S6, combining a fuzzy logic algorithm, and diagnosing the abnormal color difference condition of the cloth according to the color difference result. The cloth color difference detection accuracy and reliability are effectively improved.

The invention discloses an unmanned aerial vehicle autonomous homeward voyage charging system based on wireless charging, and relates to the field of flight control and energy management, and the system comprises the steps: monitoring the flight path, remaining power and environmental parameters of an unmanned aerial vehicle, predicting the remaining power required for completing a task, and triggering a homeward voyage condition signal and the current position information of the unmanned aerial vehicle; generating an optimal homeward flight path of the nearest wireless charging platform based on the homeward flight triggering condition signal and the current position information of the unmanned aerial vehicle; obtaining an optimal charging point based on the generated return path, and sending a deployment instruction to the mobile charging platform; the mobile charging platform advances according to the deployment instruction path, the unmanned aerial vehicle returns according to the optimal return path, the course is corrected through inertial navigation and a visual algorithm, and the landing point of the unmanned aerial vehicle is obtained. Fine adjustment and positioning of the flight attitude are formed by using the visual identification and attitude control module, and a closed-loop flight control mechanism based on image processing and attitude adjustment amount calculation is realized.

The invention discloses a visual algorithm self-training method based on multi-agent collaborative optimization, and the method comprises the following steps: constructing a multi-agent system architecture which comprises a user interaction layer, an intelligent scheduling layer, an A2A protocol communication layer and a professional agent cluster layer; the user interaction layer analyzes a user task intention and generates an execution plan; the scheduling agent calls the professional agent to complete data processing, model construction, training, testing and deployment; a task process is coordinated through a standardized communication mechanism, and task execution is supported by combining an MCP tool set, a knowledge base module and a memory system; and when the task fails, automatically executing rescheduling operation, and finally outputting a self-training result. According to the method, the development efficiency, the self-adaptability and the intelligent level are remarkably improved, and the method is suitable for computer vision tasks such as industrial detection, intelligent security and protection and automatic driving.

The invention discloses an automatic dovetail welding seam grinding control method based on robot visual positioning, and relates to the technical field of visual positioning. The method comprises the steps that after welding is completed, a dovetail welding seam image is collected and converted into a three-dimensional coordinate through a vision algorithm, and a three-dimensional point cloud is generated; smooth interpolation is performed on the three-dimensional point cloud through a B spline curve method to obtain a parameterized curve, and a control point set is optimized through a genetic algorithm to generate a global optimal polishing path; the polishing robot executes a task according to a path, a tail end sensor collects a real-time path and calculates a deviation value with a global path, and when the deviation value exceeds a preset threshold value, inverse kinematics is triggered to solve and correct the path; the hardness of the dovetail weld is measured through laser-induced breakdown spectroscopy, a comprehensive hardness value is obtained in combination with a matrix hardness database, meanwhile, a visual sensor collects the surface state, and polishing process parameters are dynamically adjusted according to the surface state; after the task is completed, the welding seam angle and flatness are detected. According to the method, the optimal path is generated through visual positioning, and automatic grinding of the dovetail welding seam is achieved.

The invention discloses a robot closed-loop joint optimization method and system based on data driving, and the method comprises the steps: carrying out the analysis of an obtained specific task description, and generating three-dimensional scene information; disassembling the specific task description into a plurality of sub-tasks, generating a sub-task execution strategy through a large language model based on the types of the sub-tasks, and executing the sub-tasks; obtaining the type of the subtask and three-dimensional scene information, and recognizing the position and posture of the target object through a visual algorithm; and fusing the position and attitude of the target object with output information obtained by executing the subtask, and continuously optimizing and adjusting a subtask execution strategy by using a reinforcement learning algorithm. The method has the beneficial effects that the training efficiency and the strategy generalization ability are greatly improved, the completion rate of tasks is remarkably improved, and the dependence on independent module optimization is reduced while the strategy accuracy is improved. According to the closed-loop system, accumulative errors are remarkably reduced, so that the robot can finish complex tasks more stably and accurately.

The invention discloses a Token-based visual task generation method, and belongs to the technical field of intelligent task automation, and the method comprises the steps: S1, cross-modal alignment; s2, performing visual Token processing; s3, constructing a task description Token sequence: constructing the task description Token sequence based on a predefined visual task template library according to requirements of a user or a specific application scene; s4, checking task feasibility; s5, task priority scheduling; s6, training a task generation model; s7, dynamic task allocation; and S8, optimizing the model. According to the method, the long sequence processing capability is optimized through a hierarchical merging strategy, the compactness of feature expression is realized while space position information is reserved, linear projection and enhanced position coding are combined to form a visual Token sequence with strong representation capability, local detail features are contained, a global context relationship is kept, and the method is suitable for the visual Token sequence with high representation capability. High-information-density feature input is provided for subsequent task processing, and the processing precision of various visual algorithms is effectively improved.

The invention belongs to the technical field of humanoid robot control, and particularly relates to a humanoid double-arm robot valve tightening method and system, and the method comprises the steps: obtaining a valve image data set based on a binocular vision camera; labeling the valve image data set to generate a labeled data set; performing enhancement processing on the annotation data; recognizing and positioning the valve based on a preset binocular vision algorithm and YOLO, and recognizing the position, the frame and the center point of the valve; the two-arm robot is moved to the two sides of the valve, and the positions and poses of the tail ends of the two arms in other directions except the clamping direction are kept consistent; clamping control is carried out based on a self-adaptive variable admittance controller; the valve is driven to rotate through rotation of the joint at the tail end of the double-arm robot, so that the valve is tightened. According to the invention, rapid and accurate valve identification is realized, and the reliability and flexibility of the system in practical application are greatly enhanced.

The invention discloses an unmanned aerial vehicle intelligent inspection system based on AI vision and a detection switch cabinet, and relates to the technical field of unmanned aerial vehicle intelligent inspection, the unmanned aerial vehicle intelligent inspection system comprises an unmanned aerial vehicle inspection platform, and the unmanned aerial vehicle inspection platform is in communication connection with the following modules: an unmanned aerial vehicle end, which is used for collecting and preprocessing video stream data of an inspection area; extracting a key frame from the preprocessed video stream data; and the AI visual analysis module is used for analyzing the extracted key frame by using an AI visual algorithm and identifying key information and abnormal fragments in the key frame. According to the invention, through the AI vision algorithm based on the convolutional neural network model, abnormal features can be automatically learned and identified, the abnormal types and specific conditions can be rapidly determined through deep analysis of the key frames, accurate positioning of abnormal segments and comparison with the preset abnormal feature database, compared with manual detection, the accuracy is greatly improved, and the detection efficiency is improved. Tiny abnormal changes can be found in time, and potential faults can be warned in advance.

The invention discloses a tunnel entrance dynamic light and signal control system based on a visual algorithm, relates to the technical field of intelligent manufacturing control, and is used for solving the problem of poor light control when the visual field of a tunnel entrance suddenly changes. According to the method, a dynamic sensing mechanism based on visual feature understanding and multi-strategy linkage is constructed, continuous monitoring and intelligent control over the driving visual adaptation state of the tunnel portal are achieved, multi-dimensional visual features are extracted through image collection and preprocessing, visual risk scores are generated, visual field sudden change and traffic abnormity are recognized in combination with a sliding window, and the driving visual adaptation state of the tunnel portal is obtained. A risk response instruction is output, illumination adjustment and signal linkage control are driven, then vehicle behavior continuity, parking probability and blind area shielding factors are fused, speed limiting, guiding or warning signals are dynamically issued, and visual model fine adjustment and strategy updating are performed based on execution feedback. And finally, illumination and signal adaptive optimization under cross-time and multi-environment conditions is realized in combination with a control scene library, and the traffic safety and control intelligence level are improved.

The invention provides a marine target real-time positioning and tracking system and method based on multi-source data fusion, and belongs to the field of target positioning and tracking. The sensor portion collects raw data. The data acquisition and time synchronization module is responsible for receiving and aligning data of each sensor; the multi-stage coordinate system registration and transformation module unifies all observation data to a world coordinate system by using IMU data and preset calibration parameters; the AI visual target detection module processes the video stream to identify a target; the adaptive federated Kalman filtering fusion module receives each path of processed data and outputs optimal target state estimation; and the dynamic task and resource scheduling module performs optimal allocation on system resources according to the current tracking state to form closed-loop control. Through deep fusion of high-precision RTK positioning data, laser ranging data, photoelectric pod attitude information and an AI vision algorithm, the positioning precision, tracking robustness and system real-time performance of a sea target under a complex dynamic sea condition are improved.

A multi-sensor furnace lining thickness measuring method for an intermediate frequency furnace adopts a visual algorithm, is realized based on a structured light camera and a laser radar, and comprises the following steps: firstly, calibrating an RGB camera and an infrared camera to obtain respective internal and external parameter matrixes; secondly, aligning the RGB camera with the infrared camera to obtain three-dimensional information; recognizing a reference target through a target detection model, extracting a plurality of groups of three-dimensional feature points in a target area, and calculating to obtain a structured light camera transformation matrix, namely, realizing pose estimation; joint calibration is carried out on the structured light camera and the laser radar; and finally, enabling a camera pose estimation result and a joint calibration result to act on laser radar data under multiple times of measurement, and enabling the two times of radar data to be in the same coordinate system, thereby realizing thickness measurement and monitoring of the furnace lining. According to the invention, under the condition that the furnace lining is not damaged, no harm is caused to the environment, the influence of the environment is small, and meanwhile, the thickness of the furnace lining can be rapidly measured and monitored in a large-scale and high-precision manner.

The invention discloses a multi-sensor fusion unmanned vehicle detection method suitable for a complex environment. The method comprises the following steps: obtaining road surface data through a laser radar and an infrared thermal imaging camera; detecting rain and snow weather conditions in real time, and calculating weather environment factors based on rainfall data and snowfall data; dynamically adjusting data weight coefficients of the laser radar and the infrared thermal imaging camera based on the weather environment factors; using a computer vision algorithm to identify an obstacle area, and extracting the distance and height of the obstacle according to the corresponding data weight coefficient; and the obstacle type is analyzed, the minimum safe distance or the minimum torque between the unmanned vehicle and the obstacle is calculated, and a driving decision is provided for the unmanned vehicle. According to the invention, the self-adaptive capability and safety of the unmanned vehicle in a complex environment are improved, the accident risk caused by sudden obstacles is effectively reduced through an intelligent early warning and intervention mechanism, and a solid safety foundation is laid for the wide application of an unmanned driving technology.

The invention discloses an automatic welding method of a reinforcement cage welding robot, and relates to the technical field of welding robots. Based on an improved ResNet-50 multi-mode visual system, the welding point identification error is reduced to 0.1 mm level, and by matching with an SE (3) space coordinate conversion algorithm, the positioning precision reaches + / -0.03 mm (3 sigma); the visual algorithm fusing the deformable convolution and the coordinate attention mechanism still keeps the recognition rate improvement of 23.6% under the illumination fluctuation of 200-1500 lux; the improved RRT algorithm is combined with the self-adaptive B-spline interpolation, so that the motion smoothness of the six-degree-of-freedom mechanical arm is improved by 400%, and the welding defect caused by path sudden change is effectively avoided; motion control of the mechanical arm is optimized through the friction compensation model, and the welding stability is remarkably improved.

The invention provides a visual algorithm model development and reasoning method and device for urban governance. The method comprises the steps of defining services and problems; collecting related data according to the business and the problem, and labeling the collected related data through a data labeling platform to obtain a data set; training a visual algorithm model based on a YOLO framework by using the data set, performing pruning, algorithm chip adaptation, model evaluation and optimization iteration on the trained visual algorithm model, and deploying the visual algorithm model into a production environment; and inputting image data to be processed, preprocessing the image data to be processed by using the visual reasoning engine, inputting the preprocessed image data into the visual algorithm model for feature extraction, performing reasoning and decision making according to the extracted features, and outputting a final reasoning prediction result. The method has the advantages that the visual algorithm model can be improved in the aspects of model generalization ability, deployment complexity, model labeling efficiency, real-time performance, prediction accuracy and the like.

The invention relates to the technical field of garbage classification detection, and discloses a garbage classification supervision system based on multi-sensor fusion, which comprises a multi-view camera, an ultrasonic sensor, an edge calculation layer and an execution layer, the edge calculation layer comprises a data acquisition module, a human body feature detection module, a garbage classification detection module and a garbage overflow detection module; the garbage classification detection module performs garbage classification detection and identification on video image data in target data based on an improved visual Transform algorithm and a front-back time sequence comparison algorithm, so that model updating is automatically started under low-confidence alert, disastrous forgetting is effectively prevented, and meanwhile, the real-time performance and high precision of the system are kept.

At present, most researches on a visual synchronous localization and mapping (VSLAM) system are established on a static scene, and a dynamic target in a real scene can seriously influence the accuracy and robustness of the VSLAM system, so that the invention provides an RGB-D visual SLAM algorithm for an indoor dynamic scene. The method comprises the following steps: (1) fusing a lightweight YOLOv8 target detection network in an ORB-SLAM3 tracking thread, performing instance segmentation on an image frame, and generating semantic information; (2) processing the semantic information by using a sliding window mechanism and a template matching strategy, identifying a dynamic target and removing dynamic feature points; and (3) a PROSAC algorithm is adopted to replace an original RANSAC algorithm to remove mismatching of feature points, and the matching performance is improved. In order to evaluate the effectiveness of the method, an experiment is carried out on a disclosed TUM RGB-D data set, and compared with an original ORB-SLAM3 and a relatively advanced dynamic DynaSLAM algorithm, the method has the advantages that the algorithm positioning precision and robustness in a dynamic environment are greatly improved, the frame rate can reach 45 fps or above under the condition of no GPU acceleration, and real-time operation can be ensured.

The invention provides a vehicle lamp entertainment interaction method and device and a computer readable storage medium, and the method comprises the steps: obtaining a live-action image of an interaction region projected by a projection headlamp in front of a vehicle, and detecting human body key points in the behavior performance of an interaction object participating in a game indicated by the live-action image in the interaction region, and analyzing and identifying through a visual algorithm, and determining the interaction behavior of the interaction object. The interaction behavior is converted into an operation instruction in a game, and the projection headlamp is controlled to project a picture indicated by the operation instruction in the interaction area, so that a user is allowed to directly control a vehicle lamp entertainment system through gestures, and the driving experience of the user is greatly enriched and improved.

The invention provides an unmanned aerial vehicle runway obstacle recognition method and an obstacle avoidance method based on improved YOLO v5, and belongs to the technical field of obstacle recognition. The unmanned aerial vehicle runway obstacle recognition method comprises the steps of obtaining a data image; and segmenting the data image by using a sky segmentation method to obtain a segmented data image. An improved YOLO v5 vision model is configured, a target recognition sub-model in a backbone network Backbone included in the improved YOLO v5 vision model is Center Net, and an MSN component is deleted from a detection head Head. And inputting the segmented data image into the improved YOLO v5 visual algorithm model, and outputting an obstacle recognition result. The invention provides an unmanned aerial vehicle runway obstacle identification method based on improved YOLOv5 for an actual application scene in which the types and the number of obstacles on a runway are relatively small in unmanned aerial vehicle runway obstacle identification, so as to solve the problems that the YOLOv5 is complex in computational power, low in efficiency, not suitable for an unmanned aerial vehicle runway and the like.

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 relates to the technical field of intelligent inspection of power equipment, and particularly discloses a power transmission line inspection system based on an autonomous correction long-endurance unmanned airship. The system aims to solve the problems of short endurance, small operation radius, single image angle, intermittent operation, leak detection, low precision, poor environmental adaptability and the like caused by dependence on manpower or GPS navigation in the conventional unmanned aerial vehicle inspection. The system comprises an energy supply module, a data acquisition module, an intelligent control module and a communication and data return module. Wherein the energy supply module is integrated with a solar energy collection, storage and intelligent scheduling unit to realize day-and-night continuous energy supply; the intelligent control module adopts an RT-DETR visual algorithm to identify a tower in real time, performs automatic deviation correction by fusing pose information, and accurately tracks a preset track; the data acquisition module supports multi-angle high-definition imaging, and the communication module guarantees real-time data return. And high-precision unmanned inspection with ultra-long endurance, full-automatic and wide-area continuous coverage is integrally realized.

The invention discloses a mechanical arm simulation system supporting industrial quality inspection, which relates to the technical field of industrial automation and comprises a multi-physics field simulation engine module, a digital twin interface module, a defect sample library module and a cooperative control module. According to the method, traditional entity mechanical arm debugging is replaced by rigid body dynamics simulation based on a physical engine, loss and production line shutdown caused by repeated trial and error of equipment are avoided, rapid construction of the digital twin environment of the production line is achieved by means of an efficient CAD model conversion process and a lightweight loading technology, and high-fidelity optical simulation is achieved through a parameterized defect model library and high-fidelity optical simulation. Various common industrial defects and complex optical effects are covered, the modeling bottleneck of a quality inspection scene is broken through, real-time closed-loop interaction of visual inspection and motion control is realized by using thread separation and an efficient communication technology, and the recognition robustness of a visual algorithm to tiny defects is guaranteed; the reliability of algorithm verification is improved through the quantitative output of algorithm performance indexes and the comprehensive test of extreme working conditions by the simulation system.

This disclosure describes an interactive augmented reality system for improving surgeon's view and context awareness during laparoscopic and video assisted surgeries. Instead of purely relying on computer vision algorithms for image registration between pre-operation (or intra-operation) images / models and later intra-operation scope images, the system can implement an interactive mechanism where surgeons may provide supervised information in initial calibration phase of the augmented reality function, thus achieving high accuracy in image registration. Besides the initialization phase before operation starts, interaction between surgeon and the system can also happens during the surgery. Specifically, patient tissue might move or deform during surgery, caused by for example cutting. The augmented reality system can re-calibrate during surgery when image registration accuracy deteriorates, by seeking additional supervised labeling from surgeons. The augmented reality system can improve surgeon's view during surgery, by utilizing surgeon's guidance sporadically to achieve high image registration accuracy.

The invention relates to a full-automatic slit lamp and an intelligent diagnosis method thereof, and aims to improve the ophthalmic examination efficiency and accuracy. The slit lamp is composed of a slit lamp device, an automatic adjusting device and a man-machine interaction device. The automatic adjusting device comprises a calculation module and an adjusting module, and the adjusting module accurately drives the slit lamp device through gear transmission and automatically adjusts operation parameters according to a preset mode. The display module of the man-machine interaction device can display the working mode, the eye image data and the interaction information, and the prompt module guides the examinee to cooperate with the examination. The camera element of the slit lamp device converts the light into image data, and the communication device transmits the data to the cloud. And during fault diagnosis, the calculation element monitors image data in real time, marks abnormities through a visual algorithm, and enters a diagnosis mode. After different modes are operated for multiple times, the calculation unit analyzes imaging result abnormity, accurately locates and displays fault components, realizes automatic fault diagnosis, and reduces maintenance cost.

The invention relates to the field of pipeline three-dimensional reconstruction and intelligent detection, in particular to a pipeline three-dimensional reconstruction and intelligent detection method based on panoramic stereoscopic vision. According to the technical scheme, a panoramic camera and a radar sensor are used for collecting panoramic images and depth data in a pipeline; carrying out denoising, distortion correction and illumination compensation processing on the collected panoramic image, and carrying out image optimization; stable feature points in the panoramic image are extracted, and feature point matching is carried out; a multi-view stereoscopic vision algorithm and a synchronous positioning and mapping technology are combined, and pose estimation and three-dimensional point cloud generation are carried out by using a panoramic image and depth data; optimizing the generated point cloud data, and reconstructing a smooth and continuous three-dimensional surface; and based on the processed three-dimensional data, intelligently detecting cracks and corrosion defects in the pipeline by using a convolutional neural network, and automatically generating defect types, positions and repair suggestions. The method is suitable for pipeline detection.

The invention relates to the technical field of transformer intelligent carrying scheme design, in particular to an AGV-based transformer intelligent carrying method and system and electronic equipment. The method comprises the steps that when an AGV is controlled to advance along a preset route, an image is acquired through an industrial camera, and a parallel bearing beam is recognized; resolving the pose of the bearing beam based on a three-dimensional vision algorithm, and fitting the pose of a bearing plane through a least square method; the AGV is controlled to accurately place the transformer according to the pose data, the AGV is automatically withdrawn along a Bezier curve path, and meanwhile, the safety distance between the AGV and the bearing beam is monitored in real time by fusing multi-sensor data. Full-process automation of transformer carrying is achieved, the problems that a traditional mode is low in positioning precision and large in potential safety hazard are solved, and the installation efficiency and reliability of power equipment are remarkably improved.

The invention belongs to the technical field of image processing, and discloses an SLM printing material defect prediction method and system based on a visual algorithm, and the method comprises the steps: synchronously collecting material image data, material parameter data and mechanical motion data in an SLM printing process; performing non-overlapping image block segmentation on the material image data, and extracting gradient information of each non-overlapping image block through a Sobel operator; based on a local search strategy, on the basis of gradient information, candidate blocks with the most similar gradient change among non-overlapped image blocks are screened by adopting a gradient difference minimization similarity matching function, and a three-dimensional image block data set is constructed; performing three-dimensional transformation processing on the three-dimensional image block data set, designing a weighting function based on human visual perception characteristics, dynamically generating an adaptive threshold, and performing differential denoising processing on the three-dimensional image block data set to obtain an image feature data set; and the material loss and the production cost can be reduced.

The invention discloses a construction personnel state monitoring and early warning system and method based on attitude tracking, and relates to the technical field of monitoring and early warning, the system comprises a data acquisition module, an attitude tracking module, a behavior analysis module and an alarm module which are connected in sequence; the data acquisition module is used for capturing a real-time video stream of a constructor; the attitude tracking module is used for extracting skeleton key points of the constructor from each frame of image in the real-time video stream by using a computer vision algorithm to obtain coordinates of the skeleton key points of the constructor; a posture recognition model is deployed in the behavior analysis module, coordinates of the skeleton key points are input into the posture recognition model, actions of construction personnel are determined, unsafe behavior detection is carried out, and an unsafe behavior recognition result is obtained; and the alarm module is used for triggering an alarm when the unsafe behavior identification result is an unsafe behavior. According to the invention, the real-time performance, precision and automation degree of monitoring and safety alarm of constructors are improved.