203 results about "Ir image" patented technology

The embodiment of the invention provides an unhooking robot control method based on multi-source visual fusion, which is applied to the technical field of robot control and comprises the following steps: acquiring an RGB image, a depth image, an infrared image and IMU data through a multi-source sensing system mounted at the tail end of a robot; carrying out feature fusion identification by adopting a double-branch neural network, and outputting the boundary contour of the lifting hook and the three-dimensional coordinates of the optimal grabbing point; the visual coordinates are unified to a robot base coordinate system through a registration correction mechanism; a Transform prediction model is constructed based on the visual and inertial signals, and future pose changes of the lifting hook are estimated; a feedforward control track is generated to counteract swing of the lifting hook, and track correction is carried out in combination with visual servo feedback; and a joint instruction is generated through path planning and inverse kinematics solution, and the mechanical arm is driven to complete precise unhooking operation. According to the method, the recognition precision, the anti-interference capability and the operation success rate of unhooking operation in complex illumination and dynamic environments are effectively improved.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing target detection method and system for a low-visibility image. The method comprises the following steps: acquiring multi-modal remote sensing image data; carrying out defogging enhancement processing on the low-visibility input image; normalizing the defogged RGB image and the defogged IR image, and then splicing and fusing the RGB image and the IR image; carrying out layer-by-layer coding on the multi-modal fusion image by adopting a mixed trunk structure fusing Transform, Mamba and CNN (Convolutional Neural Network); performing frequency domain decomposition on the trunk output features based on two-dimensional wavelet transform; generating an HR feature map by adaptively selecting a key region; and carrying out cross-scale aggregation on the HR feature map to obtain a detection target frame. Through the multi-modal image defogging enhancement and feature distillation mechanism, the definition and contrast of the remote sensing image in severe weather such as haze and rainy days are effectively enhanced, the shielding interference of environmental degradation on small target detection is weakened, and the stability and adaptability of the model in complex weather scenes are enhanced.

An image-guided diffusion network has two Convolution Neural Networks (CNNs). A RGB image and an IR image are concatenated with a Gaussian noise image and input to a denoising neural network that merges information from the RGB and IR images as noise is removed over many iterations. Then an enhancement neural network up-samples for Super Resolution (SR) and convolutes to generate a condition vector that controls Global Feature Modulation (GFM) at three convolution layers to generate a SRGFM enhanced fusion image. Timesteps are embedded using adaptive group normalization blocks within Adaptive Bottleneck Residual (ABR) blocks in the denoising network, which is a UNet having many levels of ABRs, and in the enhancement network before feature modulation. Global image features are detected by triple convoluting the image input to the enhancement network to generate the condition vector that controls feature modulation blocks at three layers of convolution.

The invention belongs to the technical field of insulation board detection, and particularly relates to an intelligent detection method and device for the bonding area ratio of an insulation board, and the method comprises the steps: collecting an infrared image of an external insulation system of a building external wall, determining a plurality of segmentation thresholds according to local regions with different sizes of pixel points, obtaining a temperature characteristic value according to the size relation between the pixel value of the pixel point and each segmentation threshold value, dividing the infrared image into a plurality of super-pixel blocks according to the pixel value and the temperature characteristic value, and calculating the temperature anomaly degree according to the number of all the super-pixel blocks contained in each heat insulation board and the concentration trend and difference of the pixel value. And determining the bonding strength of the target insulation board according to the position and area ratio of the target sub-block of which the sound velocity is within the sound velocity range of the bonding part and the bonding state is bonded in the target insulation board of which the temperature abnormal degree is greater than a preset threshold value, and judging whether the target insulation board is qualified by combining the vertical height. According to the invention, the accuracy of insulation board quality detection is improved.

The invention relates to the technical field of machine tool control, in particular to a machine tool thermal error compensation method and system based on data analysis. A machine tool thermal error compensation system based on data analysis comprises a data acquisition module, a machine tool thermal error analysis data construction module, a machine tool thermal error analysis module and a machine tool thermal error compensation module. The method comprises the following steps: acquiring a shaft temperature data set and vibration signal data of each shaft of the machine tool, current data at a servo motor of the machine tool, ambient temperature data around the machine tool and infrared images at the machine tool, and determining the running state of the machine tool under the assistance of multi-source data; weight distribution adjustment is carried out on the features related to the thermal error based on the operation state of the machine tool, the features strongly related to the thermal error under different working conditions are highlighted, the accuracy of thermal error analysis is improved, and then the thermal error compensation effect of the machine tool is improved.

The invention discloses a multi-modal target detection method based on a dual-backbone YOLO architecture, which is applied to the technical field of multi-modal target detection, and comprises the following steps: constructing a multi-modal target detection model based on the dual-backbone YOLO architecture; wherein the detection trunk comprises a double-flow feature extraction network and three fusion Mama blocks, the detection network is composed of a neck module and a head module which are used for multi-modal target detection, and the input of the detection network is the output of the three fusion Mama blocks; the double-flow feature extraction network is used for extracting local features from the RGB image and the IR image respectively; each fusion Mama block comprises a Z scanning state space channel fusion module for performing shallow feature fusion on local features and a double Z scanning state space fusion module for performing deep feature fusion on a shallow feature fusion result; and inputting a to-be-detected image to the multi-modal target detection model to obtain a target detection result. According to the invention, the target detection precision is effectively improved.

Disclosed in the present invention are a multimodal face anti-counterfeiting model training method and a face anti-counterfeiting recognition method. The model training method comprises: collecting a plurality of face images by means of a binocular camera and using the face images as a face image set, wherein a dual-mode module comprises an IR lens and an RGB lens that are arranged in a left-right alignment manner, and the face image set comprises a plurality of IR images and an RGB image corresponding to each IR image and collected in the same scenario at the same moment as the IR image; calculating a depth image on the basis of each group of IR images and the RGB images in the face image set, and adding the depth image into the face image set to obtain an updated face image set; carrying out face alignment on all the images in the updated face image set to obtain an aligned face data set; and on the basis of the aligned face data set, training a multimodal anti-counterfeiting model to obtain a trained multimodal face anti-counterfeiting model. The present invention carries out face anti-counterfeiting recognition by using the multimodal anti-counterfeiting model obtained by training, thereby improving the accuracy and security of face anti-counterfeiting recognition.

The invention relates to the field of computer vision, and discloses a cross-modal feature complementation and feature projection network system, which comprises a modal complementation module, a cross-modal feature projection module, a multi-channel feature mining module and a training and optimizing module, and is characterized in that the modal complementation module is used for reducing the modal difference between an infrared light IR image and a visible light VI S image; the cross-modal feature projection module is used for projecting feature data of different modals into a shared feature space; and the multi-channel feature mining module is used for further mining and aggregating multi-level and multi-channel cross-modal features in the shared feature space. Through the modal complementation module, the modal difference between the VI S image and the IR image can be effectively reduced, the features of coarse granularity and fine granularity are extracted by adopting a cavity convolution mode and a small convolution kernel convolution mode, modal complementation is carried out through feature weights, the complementation of modal features is realized on the premise that key information is not lost, and the accuracy of the modal complementation is improved. Therefore, the similarity of cross-modal features is greatly improved.

The invention relates to a multi-modal dynamic fusion method based on graph attention network low-rank decomposition. The method comprises the following steps: respectively decomposing local reconstruction features and global reconstruction features of different modals from an obtained RGB image, point cloud data and an infrared image by adopting wavelet transform and KA transform; constructing a first graph structure based on the local reconstruction features of different modes, and aggregating nodes in the first graph structure to obtain local fusion features; constructing a second graph structure based on the global reconstruction features of different modalities, and aggregating nodes in the second graph structure to obtain global fusion features; combining the local fusion features and the global fusion features into a combined feature tensor, and constructing an approximate combined feature tensor based on the combined feature tensor through low-rank decomposition; and inputting the approximate joint feature tensor into a Transform encoder, and pooling an output result to obtain a final dynamic fusion feature, and the method improves the accuracy and efficiency of information processing.

Thermal imaging systems are provided. An example thermal imaging system includes an infrared (IR) imager that acquires IR image data of a field of view of the IR imager. The thermal imaging system further includes video analysis circuitry operably coupled to the IR imager. The video analysis circuitry receives first temperature data of a first field reference within the field of view of the IR imager, receives second temperature data of a second field reference within the field of view of the IR imager, and receives IR image data from the IR imager. The video analysis circuitry calibrates the IR imager based upon the first temperature data, the second temperature data, and the IR image data. The thermal imaging system may further include a temperature control chamber enclosing the IR imager and configured to thermally isolate the IR imager and temperature sensors thermally coupled to the IR imager.

The present invention relates to the technical field of electronic circuit board manufacturing, and in particular to a photovoltaic silicon wafer mask lithography system, comprising an irradiation module, a feature acquisition module, a feature analysis module and a control module, wherein the irradiation module is used to irradiate resin on a silicon wafer through a mask plate to form an insoluble photosensitive resin, the feature acquisition module is used to obtain mask image information of the mask plate and an infrared image of the irradiated silicon wafer, the feature analysis module solves etching texture characterization parameters for different mask plates in combination with the etching pattern and etching depth of the mask plate, so as to classify the complex difference categories of the silicon wafer according to the etching texture characterization parameters, and the control module controls the action of the irradiation module according to the complex difference categories, thereby ensuring the etching accuracy for the silicon wafer and improving the lithography efficiency for the silicon wafer.

The invention discloses a food processing control method, which belongs to the field of power control, and comprises the following steps of: dividing a dough infrared image into a central area, a middle area and an edge area, and respectively extracting the fermentation activeness of the center, the middle and the edge; and obtaining compensation power based on a third-order feedback control model by combining the differences between the center fermentation activeness, the intermediate fermentation activeness and the edge fermentation activeness and the target activeness, thereby compensating the current heating power and accurately controlling the heating power in the dough fermentation process based on the fermentation conditions of different parts of the dough.

A leak of a cold fluid (e.g., a chilled fluid, or a fluid that is initially pressurized and becomes cold on leakage) is detected using a sequence of Infrared (IR) and Visual (VI) images. Using neural nets, in each of VI and IR image-level features are extracted from images and compared with image-level features from images of different times to obtain motion-enhanced features. The motion enhanced features from VI and IR are then compared to obtain fused features from which the leak is detected. The image-level features may be extracted using a neural net with multiple stages. The motion-enhanced and fused features may be obtained in parallel using the image-level features from the multiple stages, and the leak detection based on the stage-specific fused features.

The invention discloses a depth camera and a sweeping robot, and the camera comprises a projector which is used for projecting speckles and floodlight; the receiver is used for receiving reflection signals of the speckles and generating a speckle pattern; receiving a reflection signal of the floodlight, and generating an IR image; the RGB camera is used for obtaining an RGB image; and the processor is used for generating a depth map according to the speckle pattern or the IR image, determining a ground area according to the RGB image and / or the depth map, fusing the RGB image, the IR image and the speckle pattern corresponding to the ground area, and performing detection through a smudginess segmentation algorithm to obtain a smudginess range. According to the invention, the degree of liquid and solid smudginess can be effectively identified, and accurate segmentation and 3D modeling are carried out on the liquid and solid smudginess.

According to the target identification method and training method in the infrared image, the enhanced image features and the jigsaw image features are utilized to construct the sample pool, and the model is trained by optimizing the contrast loss between the enhanced image features and the jigsaw image features, so that the model can close different versions of the same image through contrast learning, and the target identification efficiency is improved. According to the method, the distance between the enhanced image and the jigsaw image in the feature space is determined, so that the dependence of target identification classification on annotation is reduced, and the feature extraction capability is enhanced and the classification robustness is improved by utilizing the comparative learning of the enhanced image features and the jigsaw image features.

A processor-implemented method includes generating a preprocessed infrared (IR) image by performing first preprocessing based on an IR image including an object; generating a preprocessed depth image by performing second preprocessing based on a depth image including the object; and determining whether the object is a genuine object based on the preprocessed IR image and the preprocessed depth image.

In various examples, an image processing pipeline may toggle between IR and RGB imaging modes in response to different thresholds of detected light intensity that depend on the toggling direction (e.g., a lower intensity threshold to switch from RGB to IR imaging and a higher intensity threshold to switch from IR to RGB imaging). For example, the image processing pipeline may switch from RGB to IR imaging in response to detecting an amount of light intensity (e.g., average intensity in a green channel) below a first threshold, and the image processing pipeline may switch from IR to RGB imaging in response to: (i) detecting an amount of light intensity (e.g., average intensity in a green channel) that exceeds a second threshold, and / or (ii) detecting a ratio of detected light intensities (e.g., IR to green) below a third threshold.

The invention discloses a reflective column scene abnormal depth detection method based on multi-modal fusion, and belongs to the technical field of abnormal depth detection. The method comprises the following steps: firstly, aligning RGB and IR images with different sizes, extracting a ground area in the RGB image through a segmentation network, and mapping the ground area into a depth map to accurately identify the ground; for the problem that the ground near the reflective column is difficult to distinguish, more reliable ground identification is realized by introducing a segmentation network. Meanwhile, for other abnormal noise points in the depth map, a method for screening by using the standard deviation relative difference of the two-frame depth difference and the single-frame standard deviation is provided, so that the robustness of random noise is enhanced. According to the method, the defects of an existing multi-mode method in a high-reflection scene are overcome, and a new solution thought is provided for quality improvement of the ToF depth data in a complex environment.

Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to a Registration Engine that identifies a region of interest from an infrared image portraying a target portion of a physical anatomy in a current physical pose. The Registration Engine generates a 3D point cloud of the region of interest. The Registration Engine identifies region of interest isosurface data by detecting matches between isosurface data and the 3D point cloud. The Registration Engine determines display location, at the target portion of the physical anatomy, for the region of interest isosurface data. The Registration Engine renders an Augmented Reality (AR) display of medical data with respect to the display location.

The invention relates to the technical field of intelligent household appliance control, in particular to an intelligent frying and baking equipment self-adaptive temperature control method and system based on a multi-mode sensor. The system comprises a multi-source sensing module, a feature calculation module, a confidence evaluation module, a model deduction module and a fusion control module. The system calculates temperature fluctuation and angle change rate by collecting infrared, image and angle data; the method is characterized in that a sensor confidence coefficient factor is generated by using a confidence coefficient attenuation model, and a deduction temperature is calculated by combining a heat balance equation; the weights of the measured value and the deduced value are dynamically adjusted based on the confidence coefficient, and fusion temperature is generated for closed-loop control; according to the invention, lampblack shielding and uncovering interference are effectively identified, the problem of sensing distortion is solved, and accurate temperature control in a complex environment is realized.

The invention relates to a test system for testing a LIDAR device (1), comprising a controller (5) for generating LIDAR information relating to a simulated LIDAR object based on a real object to be reproduced for a test process, wherein the LIDAR information includes at least one item of two-dimensional contour information and an item of depth information relating to a virtual distance of the simulated LIDAR object; comprising a LIDAR image generation device (6) for generating the simulated LIDAR object based on the LIDAR information; and comprising a LIDAR projection device (7) for projecting the simulated LIDAR object (9) onto a projection surface (8). The depth information is determined on the basis of a time component between the projection surface (8) and the LIDAR device (1) to be tested, which is defined by the controller (5). In one embodiment, a video image generation device can be provided, in order to generate a visible video image of at least one real object on the projection surface. The IR image which is not visible for people and which is projected by the LIDAR projection device is overlayed in a synchronised manner with a video image that is visible for people. In a variant, the test system is suitable for also testing a stereo camera, in addition to the LIDAR device, e.g. which is integrated in a vehicle to be checked, but can also be provided independently, yet in combination with the LIDAR device.

The invention discloses an abnormal behavior identification method based on a visual large model and a cognitive Agent, and relates to the technical field of computer vision, and the method comprises the steps: collecting an RGB image and an infrared thermal imaging IR image in a monitoring scene, and extracting a human body key point sequence; performing time sequence difference processing on the key point sequence, and constructing a skeleton velocity vector containing a motion trend; inputting the skeleton velocity vector into a Mamba model, adjusting model parameters by using a selective state space mechanism, and carrying out long-time-sequence modeling on the action sequence; predicting the speed variation of the next frame by adopting a residual prediction architecture and reconstructing a human body posture; and calculating an abnormal score according to the prediction error, triggering a cognitive Agent when the score exceeds a preset threshold value, calling a visual large model (VLM) to perform semantic analysis on the current scene environment, and generating a natural language warning containing an abnormal type and an environmental cause. According to the method, the forgetting problem of a traditional model in a long sequence and the jitter problem of a prediction action are solved.

A method for determining control parameters during a cardiac operation and related devices provided by the present application. During the process of perfusing cardioplegic solution into the interior of a heart model using a cardiac perfusion device, the first temperature of a target point in the heart model is obtained in real time, and the target point at least includes a target point in the myocardium; when the first temperature of each target point is lower than a first temperature threshold, the cardiac perfusion device is controlled to stop perfusing the cardioplegic solution; during the process of natural rewarming of the heart model, the second temperature of the target point in the myocardium of the heart model is obtained in real time; a first infrared image of the surface of the heart model when the second temperature reaches a second temperature threshold is obtained; the first surface temperature of the heart model is determined based on the first infrared image, and the first surface temperature is determined as the first control parameter for controlling the cardiac perfusion device to start perfusing again during a cardiac operation.

A method for removing RGBIR image noise according to an embodiment of the present disclosure comprises the steps of: acquiring an IR image and an RGB image including an IR component by demosaicing an RGBIR image; reconstructing a first RGB image from which the IR component is removed by removing the IR component from the RGB image including the IR component; and removing noise from the first RGB image on the basis of the first RGB image to which a predetermined RGB weight is applied and the IR image to which a predetermined IR weight is applied.

An electronic device comprising circuitry configured to determine a spot profile based on an intensity image obtained from a ToF image sensor or from an IR image sensor; and to perform focusing of an optical assembly based on the spot profile.

The invention discloses a cooking robot with a double-light module, which comprises a cooking robot and further comprises the double-light module arranged on the cooking robot, and the double-light module comprises a double-light assembly comprising an RGB (Red, Green, Blue) camera and an IR (Infrared) camera; the light supplementing assembly comprises a light supplementing lamp, and the light supplementing lamp is matched with the RGB camera and the IR camera; the shell comprises a shell body, the shell body is provided with a first containing cavity, the double-light assembly and the light supplementing assembly are arranged in the first containing cavity, and the RGB camera, the IR camera and the light supplementing lamp are matched with mounting holes in the shell body respectively. Due to the fact that the double-light assembly comprises the RGB camera and the IR camera, the RGB camera and the IR camera are combined, and the RGB camera is used for obtaining RGB images of food materials in the boiler barrel; the IR camera is used for obtaining the IR image of the food materials in the boiler barrel, so that the RGB image and the IR image in the cooking process are obtained, the temperature of the food materials in the cooking process can be obtained through background processing, and a data basis is provided for accurately controlling the cooking process.

The invention provides an image processing method and device and electronic equipment, and belongs to the technical field of image processing. The image processing method comprises the following steps: acquiring ambient brightness of a shooting scene; under the condition that the ambient brightness is greater than or equal to a brightness threshold value, acquiring a first RGB image shot by the first camera and a second RGB image shot by the second camera, and acquiring the ambient brightness according to the first RGB image and the second RGB image, calculating depth information of an overlapped shooting area of the first camera and the second camera in the shooting scene; under the condition that the ambient brightness is smaller than the brightness threshold value, a first IR image shot by the first camera and a second IR image shot by the second camera are obtained, and according to the first IR image and the second IR image, the brightness of the ambient light is obtained; and calculating depth information of an overlapped shooting area of the first camera and the second camera in the shooting scene. The depth information acquisition accuracy can be effectively improved.

Optical states in TIR-based image displays may be modulated by movement of electrophoretically mobile particles into and out of the evanescent wave region at the interface of a high refractive index convex protrusions and a low refractive index medium. The movement of particles into the evanescent wave region may frustrate TIR and form dark states at pixels. Movement of particles out of the evanescent wave region may allow for TIR of incident light to form bright states at pixels. The movement of the particles may be controlled by employing the drive methods of pulse width modulation, voltage modulation or a combination thereof.

A system includes an infrared (IR) light source(s); IR camera(s); an optical combiner, wherein a set of IR-responsive markers are located in at least one of: (i) within the optical combiner, (ii) on a semi-reflective surface of the optical combiner; and processor(s) configured to: control the IR camera(s) to capture IR image(s) of the optical combiner, whilst controlling the IR light source(s) to emit IR light towards the optical combiner; detect at least a subset of the set of IR-responsive markers in the IR image(s); for a given IR-responsive marker detected in the IR image(s), determine a deformation in a shape of the given IR-responsive marker with respect to a reference shape; and determine a curvature of the optical combiner, based on respective deformations in shapes of IR-responsive markers in at least said subset.

Systems, methods, and computer program products for thermal contrast determinations are provided. An example imaging system includes a first infrared (IR) imaging device that generates first IR image data of a field of view of the first IR imaging device and a computing device connected with the first IR imaging device. The computing device receives probe temperature data from a temperature probe indicative of an external environment of the imaging system and receives the first IR image data from the first IR imaging device. The computing device determines background temperature data based upon the first IR image data, determines gas temperature data based upon the probe temperature data, and determines a thermal contrast for each pixel based upon a comparison between the background temperature data and the gas temperature data. The computing device further determines a detection limit for each pixel as a function of thermal contrast.