1240 results about "Ir camera" patented technology

The invention discloses a three-dimensional scene reconstruction method, electronic equipment, a storage medium and a program product, and relates to the technical field of data processing, and the method comprises the steps: inputting each frame of multi-view image into a depth model, and obtaining a prediction depth map of each frame of multi-view image; for any target frame image in each frame of multi-view image, optimizing a local 3D Gaussian model and camera attitude information of the target frame image based on the predicted depth map of the target frame image and the depth information of the previous frame image to obtain an updated local 3D Gaussian model and updated camera attitude information of the target frame image; and optimizing the global 3D Gaussian model to obtain an updated global 3D Gaussian model based on the updated local 3D Gaussian model and the updated camera attitude information of each frame of multi-view image, and performing three-dimensional scene reconstruction based on the updated global 3D Gaussian model. According to the invention, the accuracy of a view synthesis result based on three-dimensional scene reconstruction is improved.

The invention provides an infrared scene reconstruction and rendering method and device based on three-dimensional Gaussian splashing, and the method comprises the steps: carrying out the processing of preprocessed infrared image data through employing an SfM algorithm, obtaining a sparse three-dimensional point cloud and a camera pose, and setting a Gaussian ellipsoid at the center position of the sparse three-dimensional point cloud, so as to construct a three-dimensional Gaussian point cloud model; inputting the camera pose into a Gaussian microrasterized renderer to render the three-dimensional Gaussian point cloud model to obtain a two-dimensional image of a corresponding view angle; the two-dimensional image and the infrared image data of the camera pose are compared through a loss function, the model is trained, a trained three-dimensional Gaussian point cloud model is obtained, and the loss function comprises a temperature gradient field, the projection position of a Gaussian ellipsoid in a pixel plane under the current camera visual angle and the main axis projection direction; and processing the trained three-dimensional Gaussian point cloud model by using a three-dimensional graphic engine and a 3D Gaussian rasterization renderer to obtain an infrared simulation image. And the authenticity of reconstruction and rendering of the infrared scene is relatively high.

The present invention relates to a mobile phone protective case with a rotating support, where the protective case includes a main body and a camera support assembly, the main body is provided with an accommodation cavity for accommodating a mobile phone, the camera support assembly is removably installed at a camera hole, the camera support assembly includes an installation base and a support, the support is rotationally connected to the installation base through a hinge, the support is configured to protect a camera of the mobile phone when closed, and the support is configured to support the mobile phone when opened.

Disclosed in the present invention is a photometric stereo-based device for rapidly detecting surface features of an object, capable of rapidly acquiring a plurality of images meeting the requirements of a photometric stereo algorithm. The device comprises a light emitting assembly, an acquisition assembly located above the light emitting assembly, and a data processing module; the acquisition assembly comprises a light path system and a camera group; the light path system comprises: a beam splitting prism group used for splitting reflected light from the surface of an object into a plurality of beams of light and respectively outputting the plurality of beams of light in different preset directions to form images to be processed, and a plurality of bandpass filters correspondingly arranged on the output sides of the plurality of beams of light used for forming said images, wherein the central wavelengths of the bandpass filters are respectively configured to be different wavebands of the reflected light from the surface of the object; the camera group comprises a plurality of cameras which are arranged behind the bandpass filters in a one-to-one correspondence mode and acquire filtered light beams; and the data processing module receives image information sent by the cameras, and performs compensation and photometric stereo reconstruction on the image information, so as to obtain surface features of the object. Further disclosed is a method implemented on the basis of the device.

The invention discloses an underwater environment three-dimensional reconstruction method, device and equipment and a storage medium, and relates to the technical field of underwater robots, and the underwater environment three-dimensional reconstruction method comprises the steps: transmitting laser to a target object, and collecting a laser reflection image; extracting pixel coordinates of laser stripes from the laser reflection image; calculating a three-dimensional space coordinate of the laser point based on the pixel coordinate and a camera internal reference matrix; generating a control instruction according to the depth information of the three-dimensional space coordinates, and sending the control instruction to an underwater vehicle to enable the underwater vehicle to execute equidistant diameter circular motion and collect and feed back three-dimensional point cloud data under different poses; and fusing the three-dimensional point cloud data under different poses to obtain a target three-dimensional point cloud model, and completing three-dimensional reconstruction. According to the invention, three-dimensional reconstruction of the underwater environment can be efficiently and accurately realized at low cost, and powerful technical support is provided for underwater exploration and scientific research.

The invention discloses a narrow space spraying trajectory planning method based on reinforcement learning, and the method comprises the steps: obtaining environment data through a LiDAR camera and a TOF camera, and obtaining a LiDAR point cloud and a TOF point cloud; carrying out registration fusion on the LiDAR point cloud and the TOF point cloud, and carrying out normal vector unification processing on each point of the fused point cloud; reconstructing by introducing a Poisson equation improved by shielding perception weight, and eliminating noise through Laplacian smoothing processing to obtain a target point cloud set; designing a state space, an action space and a reward function of reinforcement learning according to the planning of the spraying path trajectory, and determining a spraying path trajectory planning strategy; and based on the target point cloud set, adopting a spraying path trajectory planning strategy to carry out spraying trajectory planning. Sensor data fusion is realized through an improved iterative nearest point algorithm, and a shielding area is repaired in cooperation with a Poisson reconstruction algorithm, so that accurate correspondence between input parameters and output film thickness can be realized, and the control precision of the spraying thickness is remarkably improved.

The invention discloses a three-dimensional point cloud reconstruction method and device based on a Gaussian splash algorithm, and a medium, and relates to the technical field of three-dimensional point cloud reconstruction, and the method comprises the steps: obtaining a plurality of images; inputting the plurality of images into a pre-trained VGGT model to obtain DPT features obtained by feature coding output by a VIT encoder based on the VGGT model, and camera parameters and global point cloud data output by the VGGT model; based on the DPT features, the camera parameters and the global point cloud data, obtaining Gaussian splashing parameters corresponding to each pixel position of each image; and obtaining target three-dimensional point cloud data based on the Gaussian ball position increment in the Gaussian splashing parameters and the local point cloud data under the camera coordinates. The method is used for solving the problem that in the prior art, when multi-view three-dimensional point cloud reconstruction is carried out, the difference between simulation and a real object is large, and the difference between simulation and the real object is reduced.

The invention discloses an impeller unbalance monitoring method based on a blade three-point identification visual technology. The impeller unbalance monitoring method comprises the steps that three identification points are marked on each blade; in the working process of the wind turbine generator, the cabin clearance monitoring camera is used for shooting images of the blades in real time; identifying the pixel coordinates of the identification points in the image through an image technology, and converting the pixel coordinates into world coordinates; fitting a blade track according to the world coordinates; and identifying the impeller imbalance condition according to the blade motion trail. According to the method, image data collected by a cabin clearance monitoring camera (located at the bottom of a cabin and close to a hub shooting blade) is utilized, pixel coordinates of marking points and internal and external parameters of the camera are recognized through an image technology, the positions of the marking points in a world coordinate system are converted, and the position of the marking points is determined according to the difference of tracks of the marking points of the three blades. And identifying the impeller imbalance condition of the wind generating set.

The invention provides an unsupervised multi-view three-dimensional reconstruction method based on a bidirectional attention mechanism, and mainly solves the problem of low three-dimensional reconstruction quality in a weak texture region and a complex shielding scene in the prior art. The scheme comprises the following steps: 1) inputting a multi-view image and camera parameters; 2) preliminarily extracting multi-scale features of an input image through a bidirectional feature pyramid network Bi-FPN, taking the multi-scale features as input data, performing enhancement through an attention mechanism, and then calculating a fusion feature map; 3) performing multi-stage depth estimation from coarse to fine by using the fused feature map; 4) calculating an initial depth map of a data enhancement branch; 5) fusing multi-source information to optimize the initial depth map, and generating a final refined depth map; and 6) converting the refined depth map into a point cloud model to obtain a reconstruction result. According to the method, the multi-scale features of the image can be extracted and fused more efficiently, so that the three-dimensional reconstruction quality of a weak texture region and a complex shielding scene is remarkably improved.

The invention relates to the field of heliostat calibration, and discloses a scheduling method for calibrating heliostats through cooperation of artificial light sources and cameras in a large-scale heliostat field, and the method comprises the following steps: determining the heliostat range covered by each camera, determining the heliostat range covered by each artificial light source, and determining the heliostat range covered by each camera; the method comprises the following steps: determining the center and size of a light spot in an image shot by a camera when the heliostats are calibrated, determining the heliostats which can be calibrated in the coverage range of each artificial light source-camera combination, regarding each effective calibration combination as a node to construct an undirected graph, dividing the nodes into a plurality of groups through a graph grouping algorithm, and ensuring that the nodes in the same group are not connected with each other, therefore, all combinations in the same group can execute calibration in parallel. According to the method disclosed by the invention, the problem of affiliation misjudgment caused by light spot interference between heliostats is solved, possible shadow and shielding problems are avoided, dynamic scheduling of the artificial light source and the camera is realized, and invalid operation time is shortened.

The invention discloses a rail transit intelligent inspection robot positioning system based on laser SLAM and inertial navigation, and relates to the field of real-time positioning, the rail transit intelligent inspection robot positioning system is characterized in that data are synchronously acquired through a laser radar, an IMU, a speedometer and an RGB-D camera, and high precision in rail positioning is realized; the point cloud filtering module extracts orbit feature points through an improved voxel grid algorithm, and the inertial navigation initialization module calculates an initial pose based on IMU data and establishes a motion constraint model; the map feature layering module divides the point cloud into a track surface, equipment and a background layer; and the data tight coupling module optimizes the pose, and the dynamic object elimination module eliminates obstacles, so that the SLAM optimization precision is ensured. And the global positioning compensation module performs repositioning through particle filtering to generate a 6DoF pose estimation semantic map. The method has the advantages that by fusing the laser SLAM and inertial navigation technologies, high-precision and stable rail transit inspection is realized, the positioning precision and the environmental adaptability are optimized, and the intelligent operation and maintenance efficiency is improved.

The present invention relates to a motion amplification method, device, and storage medium based on an event camera. The method comprises: providing an optical splitter, an event camera, and an RGB camera, with the output of the optical splitter connected to the event camera and the RGB camera, respectively, to form a dual-camera imaging system; acquiring event signals via the event camera and image signals via the RGB camera; processing the acquired event and image signals through an encoder, an amplifier, and a decoder, respectively. The encoder is configured to model the features of the event and image signals and is equipped with a second-order recursive propagation module and a channel attention fusion module; the amplifier is configured to amplify the motion amplitude detected by the features output by the encoder; and the decoder is configured to model the features of the amplifier back into the image space to obtain a final motion-amplified image. Compared with existing technologies, the present invention can economically perform motion amplification and significantly improve the effectiveness of high-frequency motion amplification.

The invention provides an automatic calibration method and system for internal and external parameters of a satellite-borne binocular camera vibration test system. The method comprises the following steps: extracting and matching feature points of image sequences of a left camera and a right camera; calculating basic matrixes of the left camera and the right camera and a basic matrix between the left camera and the right camera based on the matched feature point pairs; according to the basic matrixes of the left camera and the right camera, an objective function is constructed based on the mapping relation between the essential matrix and the camera internal reference, and an IAO algorithm is adopted to solve the optimal solution of the camera internal reference; calculating a camera extrinsic parameter candidate solution and a projection matrix candidate solution according to the basic matrix between the left camera and the right camera and the camera internal parameters, screening a correct projection matrix through triangularization, and taking the corresponding extrinsic parameters as initial values of the camera extrinsic parameters; and solving the optimal extrinsic parameter of the camera by minimizing the re-projection error and the translation vector modulus length error. The method does not depend on known coordinate characteristics of the earth surface or structural characteristics of a satellite body, does not need a calibration board and manual operation, and can realize automatic real-time high-precision calibration of internal and external parameters of a satellite-borne binocular camera system.

The invention discloses a dual-machine target positioning method based on semantic prior and spatial intersection constraint. The method comprises the following steps: 1, acquiring images of a camera 1 and a camera 2, and determining the spatial orientation of a camera coordinate system relative to a reference world coordinate system; step 2, obtaining a target normalized coordinate under a coordinate system of the camera 1; step 3, obtaining a target normalized coordinate under a coordinate system of the camera 2; 4, converting the normalized coordinates of the target into geometric rays in a three-dimensional space; 5, constructing an optimization function with the minimum distance sum of squares; step 6, performing semantic-level foreground and background division on the image content through a YOLOv5 network, and constructing a three-dimensional space feature point library of a semantic background; and 7, performing single-view semantic neighborhood retrieval and depth compensation by using the three-dimensional space feature point library of the semantic background constructed in the step 6, and realizing target three-dimensional positioning in a high-altitude long-distance complex scene. According to the invention, the detection robustness and the positioning precision of the high-altitude long-distance target in the environment are improved.

The invention discloses a large-view-field high-resolution imaging system based on polarization regulation and control, the large-view-field high-resolution imaging system comprises a metasurface system array and a camera along the direction of a light path, and the metasurface system array comprises a plurality of pairs of metasurface systems which are distributed in an array; each pair of metasurface systems comprises a first metasurface system and a second metasurface system; the first metasurface system is used for performing high-resolution imaging and large-view-field imaging on a first polarization light beam and a second polarization light beam of an incident light beam respectively; the second metasurface system is used for performing large-view-field imaging and high-resolution imaging on the first polarization-state light beam and the second polarization-state light beam respectively; the camera is used for acquiring and fusing images formed by different metasurface systems so as to capture a high-resolution central region of interest and surrounding environment information of a wide field of view. The system can efficiently realize higher imaging quality.

The invention discloses a background lamina reconstruction method for a three-dimensional transient flow field based on four-dimensional tensor decomposition, and the method comprises the steps: S1, placing a background plate and a camera at two sides of a to-be-measured flow field, and shooting a distortion image of the background plate through the to-be-measured flow field; s2, representing a flow field to be measured by adopting a four-dimensional space-time grid, and decomposing the four-dimensional space-time grid to obtain a tensor decomposition module; the tensor decomposition module is used for mapping the space-time point into a feature vector, the feature vector serves as input of the multi-layer perceptron, and output of the multi-layer perceptron is the refractive index and the refractive index gradient value of the space-time point; and S3, performing Ntotal round iteration, performing light reverse tracking in each round of iteration, calculating a total loss function according to a light reverse tracking result and the distortion image, and updating the tensor decomposition module and the multilayer perceptron. The method effectively solves the problems that an existing inversion method is high in calculation complexity and poor in reconstruction timeliness, and the reconstruction speed and calculation efficiency are greatly improved.

The invention belongs to the technical field of robots, and particularly discloses a multi-mode dexterous hand fusing visual tactile sense and palmtop eye and application thereof.The multi-mode dexterous hand comprises a hand body, a visual tactile sensor, a palmtop eye module and a processor, the visual tactile sensor comprises a skeleton, an elastic body, RGB and an NIR camera, the skeleton is of a finger-shaped structure, and the skeleton and the hand body form a hand structure; the elastomer covers the outer side of the framework, and a diffuse reflection coating covers the surface of the elastomer; the RGB camera and the NIR camera are installed in the framework and used for obtaining an RGB image and an NIR image when the first object makes contact with the elastic body; the handheld eye module is installed in the middle of the hand body, comprises a second RGB camera and a TOF camera and is used for taking a second object RGB image and sparse depth; and the processor determines the first object surface three-dimensional point cloud and the second object depth map to realize pose alignment of the two. According to the invention, touch-vision-depth multi-mode perception can be realized, and the precision and robustness of the manipulator are improved.

The invention relates to the technical field of medical instrument quality detection, in particular to a medical instrument intelligent defect detection method and system based on image processing. The method comprises the following steps: acquiring a multi-angle image through a high-resolution industrial camera, and adaptively adjusting illumination and camera parameters to realize image standardization; the image is divided into local Patch, and abnormal sensitive mapping is generated through multi-scale convolution filtering, feature enhancement and fusion; generating candidate defect areas through dynamic scoring, neighborhood aggregation, boundary optimization and multi-index layering based on the mapping; carrying out defect confirmation and grading by adopting multi-model cross validation and dynamic weight fusion; historical data and environmental factors are combined to predict a defect trend through an autoregression model, and a detection strategy is dynamically adjusted to realize closed-loop optimization. According to the method, the accuracy, robustness and efficiency of small defect detection are improved, and the method has adaptive and predictive capabilities.

The invention discloses a three-dimensional reconstruction method, device and equipment based on 3D Gaussian. The method comprises the steps of performing data preprocessing based on a three-dimensional point cloud output by sparse reconstruction of a target scene, a camera internal reference and a camera pose; initializing and training a 3D Gaussian model based on a real image, the three-dimensional point cloud after candidate object mask screening, a camera pose, a depth information graph and a scale alignment parameter to obtain a coarse-grained 3D Gaussian model of the target scene; generating a dynamic target mask by taking a rendered image generated by rendering the target scene based on the coarse-grained 3D Gaussian model as a reference and combining the real image and the candidate object mask; and on the basis of the coarse-grained 3D Gaussian model, the dynamic target mask and all input data obtained by initializing and training the coarse-grained 3D Gaussian model, carrying out refined reconstruction and optimization on the coarse-grained 3D Gaussian model. According to the method, unification of high precision, high efficiency and high robustness is realized, and the increasing actual industrial application requirements are met.

The invention relates to a heading machine pose detection method based on a binocular linear camera, and belongs to the technical field of heading machine pose detection. Comprising the following steps: respectively acquiring a left imaging image and a right imaging image of an auxiliary detection plane through a left-eye linear camera and a right-eye linear camera in the tunneling process of the tunneling machine; respectively constructing four coordinate systems; determining five real points of the left imaging image and the right imaging image to obtain a left real point set and a right real point set, and calculating a carrier coordinate of each real point to form a carrier point set; determining camera coordinates of each real point in the left camera coordinate system and the right camera coordinate system to form a left camera point set and a right camera point set; mapping each real point in the right camera point set to a left camera coordinate system to form a camera point set, and determining a rotation matrix and a translation matrix between the carrier point set and the camera point set; and determining a pose detection result of the heading machine according to the rotation matrix and the translation matrix. According to the invention, the pose detection accuracy of the heading machine is high, and the precision is high.

The invention relates to the field of surveying and mapping, in particular to a laser-IMU (Inertial Measurement Unit) installation deviation calibration method based on vision assistance, a surveying and mapping method and a surveying and mapping system, a plurality of images under a plurality of laser radar calibration conditions are processed according to camera parameters and camera-IMU associated parameters, laser radar-IMU installation deviation is obtained, manual operation is avoided, and high-precision and high-efficiency deviation calibration is realized; in addition, according to the installation deviation of the laser radar-IMU, the relative space relation of the laser radar-IMU is adjusted, and accurate matching installation of the laser radar and the IMU is ensured; and according to the dynamic characteristics of the surveying and mapping path, constructing a corresponding relation between IMU data and point cloud data frames, and screening a point cloud data point which is most matched with the position point of the surveying and mapping path where each IMU data is located so as to construct a local point cloud map corresponding to the position point, thereby integrating and constructing a space map of a target area where the surveying and mapping path is located. According to the method, data omission can be reduced through local point cloud data processing and map construction, and the accuracy and credibility of spatial surveying and mapping positioning are improved.

The invention discloses a human body three-dimensional posture estimation method and system based on a multi-view scene, a terminal and a storage medium, and the method comprises the steps: determining a multi-view camera, and carrying out the video stream synchronous reading setting and camera calibration processing of the multi-view camera, and obtaining a target multi-view camera; collecting a target human body video stream through the target multi-view camera, and performing key point extraction processing on the target human body video stream to obtain initial human body key points; performing image distortion removal processing on the initial human body key points to obtain target human body key points; according to the target human body key points, posture recognition processing and three-dimensional reconstruction processing are carried out, and a human body three-dimensional posture estimation result is obtained. According to the method, the accuracy and the real-time performance of human body three-dimensional posture estimation of multiple cameras in a complex dynamic scene can be effectively improved.

The utility model discloses a multifunctional mobile platform for detecting, interfering, trapping, striking and picking up an unmanned aerial vehicle. The multifunctional mobile platform comprises a mobile platform and portable reconnaissance and striking equipment which is arranged on the mobile platform and is used for striking the unmanned aerial vehicle, the mobile platform is provided with a positioning navigation antenna for autonomous navigation; the mobile platform is provided with a navigation laser radar for ground environment detection. An unmanned aerial vehicle countering antenna and an unmanned aerial vehicle countering controller are arranged on the mobile platform, and the unmanned aerial vehicle countering controller is in communication connection with the unmanned aerial vehicle countering antenna; a pickup unit is arranged on the moving platform, the pickup unit comprises a mechanical arm mounted on the moving platform, a pickup clamping jaw assembled at the end of the mechanical arm and a 3D camera, and the pickup clamping jaw, the 3D camera and the mechanical arm are in communication connection with the unmanned aerial vehicle countering controller. According to the utility model, the unmanned aerial vehicle countering controller sends a control instruction to the 3D camera and carries out visual search on the forced landing unmanned aerial vehicle; and a control instruction is sent to the mechanical arm and the picking clamping jaw to pick the unmanned aerial vehicle.

The invention relates to the field of computer vision, and discloses a laser radar and camera joint calibration method, electronic equipment and a storage medium. The method comprises the following steps: acquiring point cloud data of a plurality of laser radars and image data of a camera; performing motion distortion compensation on the point cloud data to generate a global point cloud map; converting the global point cloud map into a plurality of three-dimensional Gaussian voxels with color attributes and covariance attributes; minimizing the first loss function by jointly optimizing the attribute of the three-dimensional Gaussian voxel and the external parameter of the camera so as to complete the calibration of the external parameter of the camera; wherein the first loss function comprises a color consistency constraint term and a rendering error term, and the color consistency constraint term is used for constraining that observed color attributes of a single three-dimensional Gaussian voxel at multiple moments and multiple visual angles are kept consistent; the rendering error term is a pixel color error between a projection image generated by the three-dimensional Gaussian voxel and image data of the camera. Therefore, the precision of external parameter calibration in a dynamic scene is improved.

The invention relates to a multi-target tracking method and system based on multiple sensors and time-space diagram self-attention. The method comprises the steps that S1, a point cloud detection target of a laser radar coordinate system and an image detection target of a camera coordinate system in the vehicle driving process are acquired; s2, obtaining a 2D-3D target matching pair according to the point cloud detection target and the image detection target, and forming a 2D-3D target matching result by an unmatched point cloud detection target and an unmatched image detection target; s3, extracting 2D image features and 3D point cloud features according to a 2D-3D target matching result, and connecting the 2D image features and the 3D point cloud features to obtain image and point cloud fusion features of the detection frame; and S4, according to the image and point cloud fusion features of the detection frame and the image and point cloud fusion features of the historical frame target, obtaining a target offset and a target and track incidence matrix, and realizing real-time tracking of multiple targets. According to the invention, the tracking precision is improved, errors are reduced, and cross-frame tracking is efficiently realized.

The utility model provides a lithium ion battery module disaster monitoring platform in a running state, which comprises a sealing test cabin, a flue gas analyzer, an infrared thermal imager and a camera, a heating coil is arranged on the inner wall of the box to ensure the consistency of the initial temperature in the box body and reduce test errors, and two lifting frames are arranged in the sealing test cabin to ensure the consistency of the initial temperature in the box body. A lithium battery module and a current sensor are arranged on the two lifting frames respectively, the lithium battery module is connected with the current sensor, and a voltage monitoring device, a resistance monitoring device and a temperature monitoring device are arranged outside the sealing test cabin. The voltage monitoring device and the resistance monitoring device are connected to the lithium battery module and used for monitoring the cell voltage and the overall resistance of the lithium battery module. According to the utility model, various operation conditions are introduced, and measures of temperature monitoring, voltage monitoring, resistance monitoring, short-circuit current monitoring and the like are combined so as to realize omnibearing and high-precision monitoring of abnormal conditions of the module.

To suppress jitter caused by a CG model that shows different motion from a camera image, and reduce discomfort of a user who views a composite image generated by synthesizing the CG model with the camera image.SOLUTION: An information processing apparatus includes: a determination means which determines position and attitude of a virtual camera; detection means which detects whether at least one of the position and attitude of the virtual camera determined by the determination means is stable or not; and acquisition means which acquires information on the stable position and attitude of the virtual camera, as reference position and attitude information. The determination means updates at least one of detected unstable position and attitude of the virtual camera, based on an attitude difference between the attitude of the virtual camera and the reference attitude.SELECTED DRAWING: Figure 8

The invention discloses a multi-modal depth sensing and grabbing system based on a transparent object, which relates to the field of robot operation and comprises a multispectral sensing module, a depth correction module, a grabbing posture generation module and a control module. The visual information and the thermal radiation information of a transparent object are comprehensively obtained by combining two perception modes of an RGB-D image and a thermal imaging (TIR) image, systematic error analysis is performed on a depth map, and error sources of an RGB-D camera and a TIR camera are detected. The system adopts an encoder-decoder model as a depth correction core, the model extracts complementary features of an RGB-D image and a TIR image through a modal exclusive encoder, feature alignment and integration are completed by using a feature fusion module, and the depth estimation precision on the transparent surface is effectively improved. And meanwhile, a Bayesian optimization method is adopted to carry out hyper-parameter optimization on the depth correction model, through hyper-parameter optimization and model fitting processing, the system effectively avoids the problems of over-fitting and under-fitting, and the robustness of the depth correction model and the transparent object grabbing precision are further improved.

The invention provides a multi-point displacement transmission-type laser monitoring system for a structure without stable observation points and a data processing method of the multi-point displacement transmission-type laser monitoring system. The system is mainly composed of a bidirectional laser transmission sensing unit, a laser transmitter and a synchronous control and data processing module. All the bidirectional laser transmission sensing units are arranged in a straight line, laser emitters are arranged at the two ends of each bidirectional laser transmission sensing unit, and emitted laser beams penetrate through all the bidirectional laser transmission sensing units. The bidirectional laser transmission sensing unit is internally provided with a semi-transparent / semi-reflective mirror window, a diffuse reflection target and a camera. The semi-transparent / semi-reflective mirror window reflects an incident laser beam to the diffuse reflection target to form a light spot, and the camera records and identifies the movement displacement of the light spot. In combination with the data processing method provided by the invention, accurate multi-point displacement monitoring of the to-be-measured structure can be realized under the condition of no stable observation point. The system is suitable for construction period deformation monitoring or engineering life cycle structure health monitoring of large civil engineering structures such as bridges, tunnels and high-rise buildings.

In some embodiments, a method for detecting if an application was initiated due to an inadvertent interaction on a user device is disclosed. The method includes analyzing signals from one or more sensors of a user device to identify any obstructions nearby. By examining the media streams, the device can derive metrics indicating whether the cameras are obstructed. The metrics combined with sensor data, can suggest that the launch was due to an unintentional interaction, causing the initiation of a first mitigation operation. This first mitigation operation involves lowering the device's power state and dimming its display. Following the initiation of the first mitigation operation, the user interface is monitored to identify interactions reflecting an intent to launch the application. If a predetermined period elapses without such interactions, confirmation of the inadvertent application launch is made, prompting the execution of a second mitigation operation.