6304 results about "Visual recognition" patented technology

The invention discloses a manipulator grabbing planning system and method based on visual identification, and relates to the technical field of robots, the manipulator grabbing planning system comprises a visual perception module, a coordinate conversion module, a motion planning module, a control execution module, a tail end perception module and a system integration and communication module; the visual perception module realizes multi-view target detection, semantic segmentation and three-dimensional pose estimation through a binocular depth camera; the coordinate conversion module is used for converting a target pose under a camera coordinate system into a world coordinate under a mechanical arm base coordinate system; the motion planning module is responsible for generating a mechanical arm grabbing path, optimizing a strategy and supporting generalization migration of a multi-form mechanical arm; the control execution module drives the six-axis cooperative mechanical arm to complete the grabbing action, and vision-force mixed feedback closed-loop control is achieved. The tail end sensing module monitors the grabbing state in real time through a touch sensor and dynamically adjusts a grabbing strategy; and the system integration and communication module is used for realizing real-time data interaction and cooperative work among the modules of the system.

The invention provides a circuit board welding fault identification method and system based on machine vision, and relates to the technical field of visual identification, and the method comprises the steps: obtaining an image of a circuit board welding region through employing a structured light projection system and a multi-camera imaging system; realizing accurate positioning of a defect region in the region of interest by utilizing three-dimensional shape information and through morphological operation and an edge detection algorithm; for the accurately positioned defect area, obtaining high-precision three-dimensional point cloud data of the defect area, extracting a multi-scale curvature feature descriptor, and fusing the multi-scale curvature feature descriptor with visual features to form a multi-feature descriptor; inputting the multi-feature descriptors into a defect classification and recognition module based on ensemble learning to obtain the types and severity of the welding defects; and according to a defect identification result, the three-dimensional space distribution and severity of the welding defects are visually displayed.

The invention provides a visual identification method and device based on computer processing, and the method comprises the steps: S1, collecting image data based on a wafer generation line, carrying out the preprocessing and label judgment of the collected image data, and obtaining a wafer defect detection data set; s2, aiming at the wafer defect detection data set obtained in S1, constructing a feature extraction and feature enhancement network to obtain a multi-level feature map; s3, designing a wafer defect detection model based on the multi-level feature map obtained in the S2, thereby obtaining a semiconductor wafer defect detection model; s4, based on the semiconductor wafer defect detection model obtained in S3, constructing a training strategy combined with antagonism collaborative learning, and training to obtain a final wafer defect detection model; and S5, model deployment: deploying the wafer defect detection model obtained in the step S4 to a production process equipment vision module, and detecting and feeding back defect problems in real time. The method has the following beneficial effects that the detection accuracy and robustness are remarkably improved while the precision is maintained.

The invention provides a DCS early warning method and system based on intelligent AI visual identification, and relates to the technical field of DCS early warning, and the method comprises the steps: obtaining the real-time operation parameters of a DCS system, and obtaining the real-time operation parameter characteristics of a unified format; inputting the real-time operation parameter characteristics into a pre-constructed intelligent AI visual identification model, analyzing the operation state of the DCS system, identifying an abnormal mode of a system level, and comparing the abnormal mode with a normal operation mode in a preset normal operation mode library; a digital twinborn simulation technology based on a physical mechanism is adopted to predict a fault mode, a fault reason and an occurrence probability of the DCS system, early warning information of a corresponding level is automatically generated according to a prediction result in combination with a preset multi-level early warning decision matrix, and the early warning information is pushed to an intelligent operation and maintenance platform of the DCS system in real time. An operator is assisted in recognizing system risks in advance, and targeted preventive maintenance measures and suggestions are given.

The invention relates to the technical field of visual recognition, and discloses a visual recognition obstacle detection method, device and equipment and a storage medium, and the method comprises the steps: carrying out the time alignment and space registration of image data collected by a visual camera in a walking-instead-of-walk robot and point cloud data collected by a laser radar, and obtaining a multi-modal data stream; inputting the multi-modal data stream into a recursive gating deep convolutional neural network for feature extraction to obtain a time sequence multi-modal fusion feature; calculating an inter-modal correlation matrix, and constructing a three-dimensional voxel grid to obtain a three-dimensional environment representation; performing two-stage detection on the three-dimensional environment representation to obtain a comprehensive obstacle representation; environment complexity evaluation is carried out according to the comprehensive obstacle representation, and a self-adaptive real-time obstacle avoidance control strategy is generated; the adaptive real-time obstacle avoidance control strategy is converted into an optimal control problem, and rolling time domain optimization is performed to obtain an optimal obstacle avoidance control sequence, so that the accuracy of obstacle detection is improved.

To provide a game machine capable of suitably changing a visual recognition mode.SOLUTION: A Pachinko machine W10 includes an upper operating member W710, an outer operation member W720, an inner operation member W730, and a lower operation member W740, and is configured such that the upper operation member W710, the outer operation member W720, the inner operation member W730, and the lower operation member W740 are displaced mutually relatively in the displacement of the mode of switching operation. Therefore, a change in a visual recognition mode of the upper operation member W710, the outer operation member W720, the inner operation member W730, and the lower operation member W740 can be made suitable.SELECTED DRAWING: Figure 925

The invention discloses a construction progress dynamic optimization method and system based on BIM and computer vision, and particularly relates to the technical field of building construction management, and the method comprises the steps: carrying out the automatic registration of a BIM model and a construction site image; processing the construction site image by adopting a visual identification algorithm to generate a visual identification result; constructing a four-dimensional dynamic BIM model, and mapping a visual identification result to a corresponding component in real time through multi-feature similarity calculation; the progress deviation is monitored by using key path dynamic identification and a deviation propagation matrix, and the risk is predicted by combining a Bayesian network and Monte Carlo simulation. The BIM and computer vision technologies are fused, a construction progress optimization system integrating automatic registration, dynamic monitoring, risk prediction and intelligent decision making is constructed, and the problems that traditional manual inspection data collection is low in efficiency, progress monitoring is lagged, risk prejudgment is fuzzy and resource allocation is extensive are solved; accurate monitoring, risk early warning and resource optimization configuration of the construction progress are realized.

The invention relates to the technical field of monitoring analysis, in particular to a park safety monitoring method and system based on dynamic visual identification. The method comprises the following steps: acquiring a plurality of park monitoring videos based on park monitoring equipment; performing three-dimensional illumination migration rendering on the plurality of park monitoring videos, and performing low-frame smooth completion so as to generate a time sequence monitoring image frame sequence; carrying out spatial position positioning calculation on the park monitoring equipment, carrying out topological association network reconstruction, and constructing an omnibearing monitoring perception topological map; performing dynamic object visual detection on the time sequence monitoring image frame sequence, and performing frame-by-frame optical flow tracking analysis to generate a dynamic target optical flow trajectory; and carrying out multi-time-point sampling on the dynamic target optical flow trajectory, and then carrying out mutation point identification so as to generate an optical flow trajectory mutation point. According to the invention, efficient park safety monitoring analysis and high-precision safety early warning are realized.

To provide a game machine capable of suitably changing a visual recognition mode.SOLUTION: A Pachinko machine W10 includes an upper operating member W710, an outer operation member W720, an inner operation member W730, and a lower operation member W740, and is configured such that the upper operation member W710, the outer operation member W720, the inner operation member W730, and the lower operation member W740 are displaced mutually relatively in the displacement of the mode of switching operation. Therefore, a change in a visual recognition mode of the upper operation member W710, the outer operation member W720, the inner operation member W730, and the lower operation member W740 can be made suitable.SELECTED DRAWING: Figure 925

The invention relates to the technical field of fire control management, in particular to a fire rescue intelligent management method and system based on a multi-mode AI large model. The method comprises the following steps: acquiring real-time environment monitoring data of a fire scene; carrying out environmental parameter analysis on the fire scene real-time environmental monitoring data, and carrying out gas change trend prediction to generate a scene gas parameter change trend; acquiring a fire scene thermal imaging image; performing temperature distribution visual identification and continuous frame temperature change rate calculation according to the fire scene thermal imaging image so as to generate a fire source area temperature change rate; performing multi-time ignition trend prediction according to the temperature change rate of the fire source area and the on-site gas parameter change trend so as to generate a multi-time ignition trend prediction map; and identifying a to-be-rescued area point based on the fire scene thermal imaging image. According to the invention, real-time and accurate distribution of on-site fire rescue resources is realized, and the rescue management efficiency and safety are improved.

The invention discloses an unhooking robot control method and system based on depth visual recognition, and relates to the field of visual recognition, and the method comprises the steps: obtaining an original optical signal and ambient light intensity, carrying out the dynamic focusing of the original optical signal, generating a target pose sequence according to a frequency domain fusion algorithm and a gradient maximization positioning algorithm, and carrying out the dynamic focusing of the original optical signal; meanwhile, the environment light intensity is substituted into adjacent frame pose difference, and a real-time speed vector is obtained; performing modeling on the target pose sequence and the real-time velocity vector by adopting neuromorphic space-time motion to obtain a predicted trajectory of a dynamic coupling target body and a space-time constraint domain of an end effector of the robot; a bionics principle is adopted to simulate a biological motion coordination mechanism, a target motion track and mechanical arm dynamic constraint are jointly described, and the limitation that track prediction and mechanical arm motion ability are disjointed in traditional isolated modeling is broken through.

To provide a game machine capable of suitably changing a visual recognition mode.SOLUTION: A Pachinko machine W10 includes an upper operating member W710, an outer operation member W720, an inner operation member W730, and a lower operation member W740, and is configured such that the upper operation member W710, the outer operation member W720, the inner operation member W730, and the lower operation member W740 are displaced mutually relatively in the displacement of the mode of switching operation. Therefore, a change in a visual recognition mode of the upper operation member W710, the outer operation member W720, the inner operation member W730, and the lower operation member W740 can be made suitable.SELECTED DRAWING: Figure 925

The invention discloses an unmanned aerial vehicle intelligent dynamic inventory algorithm, and relates to the technical field of warehouse management, the unmanned aerial vehicle flight inventory route is planned according to warehouse layout, shelf distribution and material storage area information, and non-inventory areas are marked; the unmanned aerial vehicle flies to obtain radio frequency data and material image visual data on warehouse materials; the collected radio frequency data and the material image visual data are preprocessed, feature extraction is carried out, and radio frequency features and visual features are obtained; according to the unmanned aerial vehicle intelligent dynamic inventory algorithm, through cooperative application of radio frequency and visual technology, the inventory accuracy is enhanced, radio frequency identification can accurately read material label information and obtain key data of material numbers and category codes, and a visual identification system can be used for capturing material appearance characteristics from multiple angles and multiple distances and assisting in confirming material categories and states; large-area areas in a warehouse can be efficiently checked in a short time, and smooth operation of the warehouse is guaranteed.

The invention provides a material level height early warning system and method based on point cloud and visual linkage, and relates to the field of early warning. According to the system, through cooperative work of the point cloud scanning module, the visual identification module, the data processing module, the material level height detection strategy making module, the material level detection module, the control module and other modules, all-dimensional intelligent monitoring of the bulk cargo bin is achieved. The point cloud scanning module obtains three-dimensional point cloud data of a stock bin, the visual recognition module recognizes material types, the data processing module extracts stock bin location and stock bin opening information, the material level height detection strategy module determines a differential threshold value and an early warning mechanism, the detection module monitors the material level in real time, and the control module generates a discharging strategy according to an early warning signal. The accuracy of early warning is improved.

The invention discloses a visual identification method and system, and the method comprises the steps: obtaining a visible light image, infrared thermal imaging and depth point cloud data of a target scene, and generating a time-space consistent multi-modal heterogeneous feature tensor; inputting the multi-modal heterogeneous feature tensor into a spatial frequency sensing optimizer to generate a detail-enhanced optimized feature matrix; on the basis of the optimized feature matrix, adopting an adversarial generative network to synthesize a multi-scale shielding sample, and generating an identification feature vector with enhanced adversarial robustness; inputting the identification feature vector into a self-adaptive decision engine to generate an environment self-adaptive dynamic decision parameter; and constructing a multi-scale verification pyramid according to the dynamic decision parameters, fusing the confidence score of each level through a self-correction module, and outputting a final recognition result. According to the embodiment of the invention, high-robustness and high-accuracy visual identification can be realized.

The invention relates to the technical field of image processing, and provides a training data set labeling method and device for AI visual identification, and the method comprises the steps: obtaining and processing original image data, extracting feature mapping, inputting a target detection model to obtain a preliminary labeling result, carrying out the analysis, and determining a clustering center and a candidate target set; the candidate target set is classified based on the clustering center, the confidence degree of the classification labeling result is calculated, labeling data with the confidence degree higher than a threshold value serve as high-confidence-degree labeling data, rechecking and consistency checking are conducted on data with the confidence degree lower than the threshold value, and consistency labeling data are generated; and the target detection model is optimized in combination with the high-confidence data, the low-confidence data is re-labeled by using the optimization model, and the high-confidence data and the re-labeled data are combined to generate a complete labeled training set to train the recognition model. Through the above scheme, the consistency and confidence of the data are ensured, and the problems of inconsistent labeling and missed labeling and false labeling in complex scenes and multi-target recognition are effectively improved.

The invention discloses a stacking machine motion speed parabolic trajectory deviation correction comprehensive method, and relates to the technical field of stacking machine trajectory deviation correction, and the method comprises the following steps: carrying out the data collection of the real-time motion trajectory of a stacking machine through an acceleration sensor, an encoder and a visual recognition system, building a trajectory deviation detection model, and carrying out the calculation of the trajectory deviation detection model; the motion speed, the acceleration and the position information are extracted, and the deviation value of the current trajectory deviating from the ideal parabolic trajectory is calculated. According to the invention, through multi-sensor fusion and Kalman filtering, the accuracy and stability of track correction of the stacker are improved; an LSTM neural network is adopted to predict inertial errors, and correction failures are reduced in combination with an adaptive compensation strategy; a double-closed-loop control and anomaly detection mechanism is introduced, intelligent safety protection is achieved, the fault recovery capacity is improved, it is ensured that the stacking machine stably operates in a complex environment, and the reliability and working efficiency of an automatic warehousing system are enhanced.

The invention relates to the technical field of water conservancy projects, in particular to a water conservancy project crack detection method and system based on intelligent visual identification, and the method comprises the following steps: obtaining a crack image, enhancing the brightness and contrast in different regions, extracting and optimizing the crack edge, executing morphological processing, denoising and smoothing, segmenting a crack region, and judging the connection condition. And analyzing the geometrical characteristic detection connectivity, and calibrating the crack position to obtain a positioning result. According to the method, through dynamic adjustment of image brightness and contrast, the crack is more prominent in a complex background, especially under non-uniform illumination, crack identification difficulty caused by illumination inconsistency is reduced, crack edges are optimized through morphological operation, the missing part of the crack can be filled, the crack contour can be smoothed, and noise can be eliminated. And in the crack segmentation stage, through self-adaptive threshold and geometric feature analysis, the fracture and connection conditions of the crack are accurately judged, and positioning of the crack end point and the continuous area of the crack end point is achieved.

The invention provides a tunnel surrounding rock deformation monitoring system and method based on visual identification, and relates to the technical field of tunnel surrounding rock deformation monitoring. According to the method, firstly, point cloud data are analyzed to extract crack features, and image data at historical moments are used as input to construct a visual prediction model based on a convolutional neural network, so that the deformation at the current moment is obtained. Then, the deformation amount, the temperature and the strain data at the current moment are combined to generate a deformation index used for reflecting the abnormal deformation degree. By comparing the deformation index with the preset deformation threshold value, the system can judge whether abnormal deformation exists in the monitoring area or not in time. If the deformation index is lower than a threshold value, marking as normal; and if the threshold value is exceeded, marking as abnormity and sending out early warning. The system provides an efficient and real-time tunnel surrounding rock deformation monitoring solution.

The invention relates to the technical field of fabric detection, and discloses a fabric defect intelligent detection method and system based on AI visual identification. According to the method, motion blur is quantized through motion state data, optical blur caused by fabric motion is eliminated through deconvolution solution, so that motion interference in the fabric transmission process is processed in a targeted mode, self-adaptive balance of the deblurring capacity and the feature retention capacity is achieved, and then based on the optical interference principle, the deblurring capacity and the feature retention capacity are improved. Through a dynamic calibration system combining hardware-level real-time compensation and multi-dimensional optical parameter calibration, dynamic optical parameter calibration of primary correction data is realized, then fabric defect characterization data is extracted to accurately obtain defect features, and finally, a detection-production line control closed loop is constructed through a quality quantitative index and a comprehensive risk value, so that fabric defect detection is realized. The fabric defect detection precision can be improved, so that the problem of high defect missing detection and false detection rate caused by optical data distortion due to movement and environment interference in a traditional method is effectively solved.

The invention relates to the technical field of visual identification, and discloses a belt tearing detection method, device and equipment based on visual identification, and the method comprises the steps: carrying out the dual-channel image collection and preprocessing of the surface of a conveying belt, and obtaining a multi-channel preprocessing image; extracting a thermal difference feature and a texture structure feature of the multi-channel preprocessed image through a double-flow feature network, and inputting the thermal difference feature and the texture structure feature into a quaternion material deformation analysis model for deformation gradient tensor and invariant parameter calculation to obtain a tear feature description vector; performing hierarchical progressive identification and three-dimensional reconstruction analysis to obtain target tearing feature data; and risk assessment is carried out based on the target tear feature data to obtain a tear grade classification result, the interference of ambient temperature drift and non-uniform illumination is effectively eliminated, the calculation efficiency and accuracy of tear detection are improved, and false alarms and missing alarms are reduced.

The invention discloses an industrial machine vision recognition and analysis system, relates to the technical field of machine vision recognition, and is used to solve the problem of poor industrial machine vision recognition. The invention combines total variation denoising, contrast-limited adaptive histogram equalization, random geometric transformation and other technologies to enhance image diversity, improve the system's generalization ability for images in different environments, extract image texture and edge features, provide high-quality data for subsequent analysis, use unsupervised learning to extract image potential features and patterns, and locate abnormal areas through template matching, so as to adaptively discover defects in the production process, dynamically update the model based on a small amount of labeled data, optimize feature representation in real time, and adapt to environmental changes. Finally, semi-supervised learning and pseudo-label generation technology are used to expand the training set, improve the generalization ability of the model, maintain efficient learning when labeled data is limited, reduce manual labeling costs, and improve the accuracy of automated quality inspection.

The invention relates to the technical field of automatic loading and logistics control, in particular to an automatic powder loading device based on visual identification, which comprises a visual identification module, a flexible grabbing module, a transfer module and a stacking module, the form, internal distribution and material characteristic data of the powder bag are obtained through a multi-mode sensor, a grabbing path is generated, and grabbing operation is completed through a flexible manipulator; the transfer module dynamically adjusts the transportation state of the powder bag in combination with path optimization and posture monitoring; the stacking module monitors stress of a stacking layer in real time on the basis of the stacking thermal distribution diagram, adjustment suggestion data are generated, stacking position and angle optimization is completed through a self-correction unit, and the stability of a stacking structure is ensured; the powder loading efficiency, the stacking precision and the long-term stability are remarkably improved, and the method is suitable for efficient logistics management of complex loading scenes.

The invention relates to the technical field of welding control, in particular to a welding control method and device for a signboard. The method comprises the following steps: acquiring a high-definition monitoring image and a welding work log of a signboard to be welded; performing signboard texture visual identification on the to-be-welded signboard high-definition monitoring image, and performing welding visual effect simulation to obtain a signboard welding visual effect; welding points are accurately recognized based on the signboard welding visual effect, and a dynamic welding path is planned, so that the dynamic welding path is constructed; performing real-time welding control on the signboard to be welded based on the dynamic welding path, and then performing smooth optimization on the motion track of the welding gun so as to construct an optimal smooth motion track of the welding gun; current real-time working parameters of the welding gun are obtained; and self-adaptive welding gun parameter adjustment is conducted on the current welding gun real-time working parameters based on the optimal welding gun movement smooth track, and therefore self-adaptive welding gun adjustment parameters are obtained. The signboard welding quality is improved, and welding errors are reduced.

The invention discloses a waste plastic classification method and system based on visual identification, and particularly relates to the technical field of plastic classification. Reflection suppression is realized through an industrial camera equipped with a polarization filter and a multi-angle cross polarization light source, and the structure and color characteristics of a shielded area are effectively restored by combining brightness equalization, reflection area detection and image restoration technologies; and then semantic segmentation and target classification are completed by using an improved deep neural network model, a control signal is generated based on an identification result, and an execution mechanism is driven to realize accurate sorting of multiple types of plastics, so that the identification robustness and the sorting efficiency of the system under a complex illumination condition are improved.

The invention provides an intelligent industrial quality inspection method and system based on AI visual identification, and relates to the technical field of visual identification, and the method comprises the steps: inputting an industrial product image collected at an edge end into a pre-trained defect region detection model, and obtaining a defect region detection result; extracting defect area features according to a defect area detection result, carrying out dimension reduction processing to obtain compressed defect area features, and deciding to unload a defect classification task to a cloud end or directly carry out defect classification at the edge end according to an adaptive task unloading strategy in combination with working data of the edge equipment; if the defect classification task is determined to be unloaded to the cloud, transmitting the compressed defect area features to the cloud through a network, performing defect type identification on the cloud by adopting a pre-trained defect classification model, outputting a classification result of the defect area, returning the classification result to an edge end, and combining a defect area detection result and the classification result of the defect area to obtain the defect classification task. And generating a defect visualization effect picture and a quality inspection report.

The invention discloses a multi-modal collaborative security monitoring method, device and equipment and a storage medium, and relates to the technical field of computer vision and sensor fusion, the method comprises the following steps: collecting multi-modal data of a monitoring area, the multi-modal data comprising image data, infrared temperature data and environmental parameter data; based on the target detection model, detecting personnel security features and environment security features in the image data, and outputting a visual identification result and visual identification confidence; and when the visual identification confidence coefficient is lower than a preset threshold value and the consistency of the multi-modal data meets a synchronization condition, performing association verification of the multi-modal data by adopting weighted fusion in combination with the infrared temperature data and the environmental parameter data to perform secondary identification of a target so as to output the multi-modal fusion confidence coefficient. Through combination of visual identification and a multi-sensor fusion technology, accuracy and robustness of personnel and environment safety identification in a complex industrial environment are improved.

The invention discloses an operation and maintenance manipulator intelligent control method and system based on visual identification, and relates to the technical field of intelligent manipulator control, and the method comprises the steps: collecting RGB image data and depth image data of an operation and maintenance operation area, and obtaining a standardized image matrix and a mapping relation matrix; inputting the standardized image matrix into an improved ResNet residual network model, generating a comprehensive feature descriptor, and calculating a spatial position coordinate and an attitude angle of the target equipment based on the mapping relation matrix; based on the current joint angle state of the manipulator, an improved Jacobian matrix inverse kinematics algorithm is used for solving a target angle sequence of each joint, a preset operation mode library is matched based on the comprehensive feature descriptor, and a grabbing force parameter and a motion speed parameter are determined; and converting the target angle sequence into a control instruction, and sending the control instruction to each joint driver of the manipulator to drive the manipulator to complete action planning. According to the invention, full-process automation from environment perception to task execution is realized.

The invention relates to the technical field of bridge construction, in particular to an intelligent cleaning device and method for a box girder side formwork. The device comprises a mobile platform, a control device, a mechanical arm assembly, a cleaning device, an infrared induction sensor, a dust collection device, a multispectral visual recognition module, a 3D environment modeling module, a path planning module, a cleaning quality detection module and a self-adaptive learning database. The multispectral visual recognition module recognizes the stain type and the adhesion strength. The 3D environment modeling module is used for constructing a three-dimensional point cloud model of a working area in real time; the path planning module generates an optimal cleaning path based on a reinforcement learning algorithm; the cleaning quality detection module detects residual stains and triggers secondary cleaning; the adaptive learning database stores historical cleaning data and optimizes a cleaning strategy through a machine learning model. The full-process automatic cleaning operation of the box girder side form can be achieved, and the comprehensive functions of dirt recognition, autonomous path planning, efficient cleaning, real-time detection and the like are achieved.

The invention discloses a driver fatigue driving state recognition method based on deep learning, and relates to the technical field of computer vision recognition methods, and the method comprises the following steps: S01, multi-modal data collection, S02, data preprocessing, S03, data enhancement, S04, multi-modal feature fusion, S05, deep learning model construction, S06, fatigue driving state recognition, and S07, real-time feedback and optimization. S08, a fatigue driving state recognition result is output, the fatigue characteristics of the driver can be captured from multiple dimensions by fusing multi-modal data including video images, vehicle driving data, environment data, voice signals and physiological signals, compared with the prior art, the scheme can more comprehensively analyze the fatigue state of the driver, and the fatigue driving state recognition result is obtained. Therefore, traffic accidents caused by fatigue driving are reduced.