8728 results about "Imaging equipment" patented technology

The present invention relates to the technical field of mine safety management, and discloses a method and system for dynamic monitoring and early warning of mine environment, including the following steps: multi-source data acquisition, collecting dynamic data of mine environment through remote sensing imaging equipment, ground sensors and meteorological stations; data preprocessing, noise cleaning, time alignment and spatial reference coordinate unification of the data; spatiotemporal data fusion, mapping multi-source data to a unified feature space through feature extraction, cross-modal fusion and attention mechanism; intelligent analysis and disaster prediction, predicting surface deformation, landslide risk and tailings dam stability in mining areas based on multi-task joint learning models; risk assessment and early warning response, dividing risk levels according to comprehensive risk scores, and dynamically generating early warning information, evacuation paths and dangerous area isolation boundaries. The present invention solves the problems of difficulty in multi-source data fusion, low disaster prediction accuracy and delayed early warning response in the prior art.

A method of self-testing an imaging system of a sample handling apparatus is provided. Systems and non-transitory computer readable mediums performing the method are also provided.

A system and method of view restoration include a computer-assisted device having an imaging device and a controller coupled to the imaging device. The controller is configured to record kinematic information, imaging information, or both the kinematic information and the imaging information before movement of the imaging device from a first repositionable arm to a second repositionable arm or from a first workspace port to a second workspace port; detect the movement of the imaging device from the first repositionable arm to the second repositionable arm or from the first workspace port to the second workspace port; determine, in response to the detection, a desired position and orientation of the imaging device based on the recorded kinematic information, the recorded imaging information, or both the recorded kinematic information and the recorded imaging information; and move the imaging device based on the desired position and orientation.

A system directs an imaging device to continuously capture visible light and fluorescence illumination from a surgical area. The system generates a visible light image stream based on the captured visible light and a fluorescence image stream based on the captured fluorescence illumination. The system operates in a first display mode by directing a display device to display a first video stream based on a first set of at least one of the visible light image stream and the fluorescence image stream. In response to detecting an event that occurs within the surgical area, the system switches from operating in the first display mode to operating in a second display mode by directing the display device to display a second video stream based on a second set of at least one of the visible light image stream and the fluorescence image stream, the first Instructions set being different than the second set.

The invention provides a semiconductor defect detection and process optimization method based on deep learning. The method comprises the following steps: acquiring semiconductor wafer surface multispectral image data, wafer reflectivity distribution data and thickness distribution data through multispectral imaging equipment; performing multi-modal fusion preprocessing to generate a defect enhanced image; extracting micro-nano scale topological features of the surface of the wafer through a lattice structure guided deep learning network; performing frequency domain-space domain joint modeling on the topological features to generate a defect feature energy distribution diagram; fusing real-time process parameter data to dynamically classify defect features and output defect types and confidence coefficients; generating a process equipment control instruction according to the defect classification result; a defect cause knowledge graph is constructed through a causal inference engine, and a process optimization scheme is output; and feeding back the optimized process parameters to the semiconductor manufacturing equipment to form closed-loop control. The semiconductor manufacturing efficiency and the product quality can be improved, and the production cost is reduced.

The invention discloses a multi-sensor fusion positioning mapping and thermal imaging device and a computer readable medium. The equipment integrates positioning mapping sensors such as a thermal imaging camera, a visible light camera, a laser radar and an inertial navigation system, and is specially designed for high-risk and complex fire emergency environments. Through an end-cloud integrated cluster cooperation system and a multi-link stable transmission technology and in combination with intelligent decision support, high-precision real-time positioning and dynamic 3D temperature measurement mapping can be realized in an environment in which satellite navigation signals are weak or unavailable. The multi-mode portability and environmental adaptability design of the system ensures the high efficiency and safety of an operator / unmanned system under extreme conditions, and the advanced data processing and analysis technology of the system significantly improves the accuracy and response speed of rescue operation. Due to the innovative characteristics, the method has remarkable practical application value and market prospect in the field of fire-fighting emergency rescue.

A method of self-testing an imaging system of a sample handling apparatus is provided. Systems and non-transitory computer readable mediums performing the method are also provided.

The invention discloses a fault detection method for high-resolution infrared thermal imaging display, and relates to the technical field of power equipment monitoring. According to the fault detection method for the high-resolution infrared thermal imaging display, a thermal imaging image of the surface of power equipment is obtained through high-resolution infrared thermal imaging equipment, dynamic correction is carried out on a temperature field in combination with environmental parameters and load current, and an interference-free temperature matrix is generated. The method comprises the following steps: modeling a heat conduction relationship between equipment by using a graph neural network, detecting a temperature abnormal region through a U-Net segmentation network, and generating an abnormal region mask and temperature space features; the time sequence correlation between temperature fluctuation and electrical parameters is analyzed through a dynamic time warping algorithm, and continuous faults and instantaneous interference are distinguished. And based on small sample transfer learning, in combination with a pre-training feature extractor and an attention mechanism, fault type classification is carried out, and fault types and confidence coefficients are output. Therefore, efficient and accurate detection of power equipment faults is realized.

The invention relates to the technical field of photovoltaic panel cleanliness detection, in particular to a photovoltaic panel cleanliness detection method which comprises the steps of data acquisition, image preprocessing, pollution feature extraction, cleanliness index calculation, cleanliness judgment, data output and result recording and feedback. According to the invention, pollution data is acquired through imaging equipment, and adaptive filtering and morphological analysis are combined, so that precise pollution feature extraction, detection precision improvement and system real-time calculation of a cleanliness index are realized, pollution conditions are fed back through a cloud platform or an Internet of Things interface, and the power generation efficiency of a photovoltaic panel is ensured. The system can also automatically trigger a cleaning device and control the cleaning process according to a cleanliness threshold value, full-automatic management is achieved, the labor burden is relieved, in addition, a machine learning algorithm automatically adjusts the threshold value according to environment monitoring data and seasonal changes, and parallel detection further optimizes the efficiency, shortens the detection time, improves the operation and maintenance effect and prolongs the service life of the photovoltaic panel.

A processor captures a first image of a platform using a first camera of a plurality of cameras and determines a color metric based on the first image, wherein the color metric indicates a color of the first image. The processor compares the color metric to the color metric. In response to determining that the color metric associated with the first image does not match with the color setpoint, the processor adjusts brightness of at least one of a first set of warm LEDs or a second set of cool LEDs to cause the color of the light incident on the platform to match the color setpoint.

A system includes a plurality of satellites orbiting around a celestial body in a plurality of orbital planes. Each satellite includes an imaging device having a field of view (FOV) to capture an image of a sky that includes celestial image features of resident space object (RSO), stars, and / or planets. The satellite processor is configured to input the image data for each image into a pre-processing software pipeline to generate for each image a contrast-enhanced image data replica of each image, and an enhanced image data replica of each image, to receive from an output of a known-unknown RSO split data processing pipeline, a determination that a candidate RSO is a known RSO stored in an RSO catalog, or an unknown RSO, and to assign to the candidate RSO based on the determination, an RSO ID of the known RSO listed in the RSO catalog, or a new RSO ID.

Ultrasound imaging devices, systems and methods are provided. An intraluminal ultrasound imaging system includes a patient interface module (PIM) that communicates with an intraluminal imaging device, the intraluminal imaging device including an ultrasound imaging component, the PIM including a first reconfigurable logic block, a configuration memory and a processing component, the first reconfigurable logic block including a first plurality of logic elements interconnected by a first reconfigurable interconnect element, the configuration memory being coupled to the first reconfigurable logic block, the processing component being coupled to the configuration memory, the processing component being configured to: detect device properties of the intraluminal imaging device communicating with the PIM; and load at least one of a first configuration or a second configuration into the configuration memory based on the detected device properties to configure one or more first reconfigurable interconnect elements in the first reconfigurable interconnect element so that the first plurality of logic elements are interconnected to communicate with the ultrasound imaging component.

The invention provides a heat input regulation and control method and system for electric arc additive continuous forming, and relates to the technical field of additive manufacturing, and the method comprises the following steps: collecting process parameters in real time, inputting pretreatment process data into a self-adaptive heat input quantity calculation model, and constructing and calculating to obtain a real-time heat input quantity based on a thermodynamic principle and a heat transfer theory; starting the multispectral imaging equipment, performing real-time temperature field scanning, recording temperature field scanning data, extracting temperature gradient and cooling rate information, performing correlation calculation with real-time heat input quantity, and determining an influence relation evaluation result; generating a heat input sequence according to the influence relation evaluation result; and inputting the heat input sequence into a self-adaptive control system constructed based on a reinforcement learning algorithm, calculating an optimal parameter scheme according to a preset target sedimentary layer performance index and a current temperature field state, adjusting corresponding spatial-temporal distribution of control energy input according to the optimal parameter scheme, and performing control heat input on different positions and different layers of the sedimentary layer.

The invention relates to a fabric flaw detection and traceability system based on edge calculation and computing power scheduling, which is suitable for intelligent quality control in a textile production process. The system comprises an acquisition unit, a modeling unit and the like. The acquisition unit acquires fabric images and environmental data through a multispectral imaging device and a process parameter sensor, and constructs time-aligned multi-modal feature tensors. The modeling unit extracts texture features by using unsupervised comparative learning in combination with fabric material characteristics, and generates potential texture fingerprint vectors. And the detection unit adopts a target detection network of a channel attention mechanism to identify fabric flaws and output positions, types and severity. The traceability unit analyzes correlation between defects and process parameters through time sequence causal reasoning, and constructs a causal atlas. And the optimization unit generates a process optimization vector according to the causal atlas and the risk score, and realizes visual display and edge control feedback, thereby constructing a real-time defect control and explainable traceability-oriented closed-loop quality management system.

The invention relates to the technical field of agricultural science and technology, in particular to a leaf disease and pest identification method and system based on image processing, and the method comprises the following steps: setting multispectral and thermal imaging equipment, collecting multispectral images and thermal imaging data of a target frequency band, deleting images with insufficient definition and data with excessive noise, obtaining optimized multispectral images, and obtaining the leaf disease and pest identification result. And marking and classifying the data according to the collected date to form a time sequence image data set. According to the method, the network parameters are dynamically adjusted to reflect the state of each stage of leaf growth, so that the pest and disease damage prediction is not only based on static data, but also reflects the actual change of plant growth, and the real-time application value and accuracy of the prediction model are improved; the introduction of state transition learning enables the transition from health to each stage of pest and disease damage to be predicted more accurately, carries out early diagnosis and timely intervention, improves the recognition precision of the specific position of the pest and disease damage, can carry out local processing, and effectively improves the pertinence and economic efficiency of the processing.

The invention relates to an optimized method for medicament preparation. A machine learning model of a deep neural network is adopted to perform screening and combinatorial optimization on active ingredients and auxiliary materials of the medicament, and a relation model among the ingredients, the drug effect and the stability is established; a dynamic blending module is adopted to adjust the component proportion and dosage in real time according to different medicament preparation conditions including temperature and pH value; constructing a self-adaptive reaction condition adjusting system, and monitoring reaction conditions including temperature, pressure, pH value and stirring speed in the preparation process in real time; a dynamic mixing system is introduced, and a microfluidic technology is adopted to control metering and mixing of pharmaceutical components; a medicament preparation process is decomposed into a plurality of key steps including primary reaction, purification and concentration, and each step is independently optimized; a real-time quality monitoring system is constructed, and the physical properties, including particle size distribution, solubility and purity, of the medicament are monitored in real time through a sensor and imaging equipment; and optimizing the integration platform in the whole process.

The invention discloses an AI-based neurosurgery auxiliary robot visual positioning system, and relates to the field of visual positioning, which comprises the steps of deploying and initializing structured light scanning equipment and near-infrared imaging equipment, carrying out multi-modal image acquisition on a surgical area, and carrying out standardization processing, space-time alignment and fusion on the acquired image. A multi-modal visual acquisition and preprocessing module, a three-dimensional tissue model construction and registration module, a visual-anatomical feature recognition and extraction module, an AI auxiliary positioning and path optimization module, an intraoperative dynamic perception and feedback control module and a target position confirmation and instruction output module are constructed. According to the method, high-precision identification and dynamic modeling of a brain tissue structure are realized, real-time identification, path planning and position correction can be carried out on a key anatomical structure in an operation process, the precision, the intelligent level and the intra-operation response capability of neurosurgery operation are remarkably improved, the operation risk is effectively reduced, and the positioning reliability and the automatic control efficiency are improved.

The invention relates to an image segmentation method for a burn wound area, and the method comprises the steps: collecting burn wound images under different spectrums through a multispectral imaging device, and enabling the burn wound images to comprise a plurality of wavebands, namely visible light, infrared light and ultraviolet light; for each spectral image, analyzing reflection and absorption characteristics of a burn area under different wavebands by using optical characteristics of biological tissues, and marking a potential burn area; designing a self-adaptive algorithm, and dynamically adjusting parameters, including exposure and light intensity, of imaging equipment based on feedback of different spectral characteristics; the feature contrast of the burn area is enhanced; preliminarily delimiting the boundary of the burn region according to the enhanced spectral image through a threshold segmentation or region growing algorithm; inputting the multispectral image into a hierarchical processing model, and decomposing different tissue levels of the burn area including epidermis and dermis based on different wave band or color information; on each layering result, a feature extraction algorithm including texture analysis and edge detection is used for extracting detail features of each layer.

The invention discloses a casting product appearance defect detection method and system based on machine vision, and particularly relates to the technical field of casting product defect detection. According to the method, casting materials of a production line are divided into different categories, the casting materials with abnormal absorptivity are identified, terahertz wave scanning is carried out on the casting materials, scattering images are obtained, and the stability of scattering point density distribution is evaluated; scanning by using a terahertz time-domain spectrometer, analyzing an amplitude fluctuation state of a time-domain signal and evaluating a waveform distortion degree; after comprehensive analysis, the quality of images captured by the imaging system is evaluated, the images are divided into different grades, defect detection is directly carried out on high-quality images, the quality images are centered, the accuracy of the images is analyzed, and the transmitting power and the scanning speed of terahertz imaging equipment are dynamically adjusted, so that the imaging quality is improved, and the accuracy and the efficiency of detection are remarkably improved; and the quality and safety of casting products are ensured, and the product quality problem and potential equipment faults caused by untimely defect detection are effectively avoided.

A consumer-operated kiosk can include an inspection area configured to receive a first device from a user. The kiosk can include an imaging device configured to image the first device when the first device is positioned within the inspection area. In some embodiments, the kiosk includes a vending apparatus configured to store a plurality of second electronic devices for purchase. The kiosk can include one or more release mechanisms configured to directly release any one of the second electronic devices from the vending apparatus. The kiosk includes one or more processors operably connected to the inspection area, the imaging device, the vending apparatus, and the one or more release mechanisms. The one or more processors can be configured to cause the imaging device to capture one or more images of the first electronic device, facilitate purchase of the first electronic device based at least in part on the one or more captured images of the first electronic device, receive a request to vend a selected phone from among the phones in the vending apparatus, and / or cause the one or more release mechanisms to release the selected phone directly from the vending apparatus into a vending bay of the kiosk.

The invention discloses a hidden camera detection method and system based on a cat eye effect, computer equipment and a storage medium, and relates to the technical field of laser imaging, and the method comprises the steps: sensing ambient light, carrying out the initialization, and carrying out the parameter adjustment in real time; transmitting a signal through a multispectral light source, and receiving a reflected signal for multiband imaging; carrying out adaptive binarization processing on the image, and calculating a local gray scale probability distribution matrix of the image; and calculating a suspected degree for target detection, and giving an alarm when a camera is recognized. According to the hidden camera detection method based on the cat eye effect, through automatic switching of multi-band light sources, the types of the light sources are optimized, detection light of different wavelengths is emitted, the broad spectrum range is covered, the reflection characteristics of various hidden imaging devices are dealt with, detection is carried out according to imaging of different light sources, and the detection accuracy is improved. The method can be used for detecting the anti-reconnaissance hidden imaging equipment.

The invention provides a processing method and system for reconstructing an LDI sub-pixel image through sparse compression and application, and the method comprises the steps: calculating a low-resolution to-be-exposed image corresponding to each phase structure in advance through a computer image compression algorithm; the low-resolution to-be-exposed images obtained through calculation are loaded to the digital micro-reflector according to the time sequence, and the display moment of each pattern is matched with the rotation position of the corresponding phase structure; coupling the pattern of the digital micro-mirror and the phase encoding mask in a frequency domain through a 4-f optical system, and reconstructing a high-resolution exposure pattern at a sub-pixel level by utilizing a diffraction effect; projecting a high-resolution light field of the reconstructed high-resolution exposure pattern to the surface of the photoresist, and forming a target circuit pattern by accumulating exposure dose; and the micro-nano structure with sub-pixel precision is obtained after development. The system comprises an image compression module, a pattern matching module and a pattern forming module. According to the invention, the manufacturing cost of the laser direct imaging equipment is reduced under the condition of the same precision.

The invention discloses a defoaming agent foam distribution analysis method based on image feature recognition, and particularly relates to the field of industrial foam behavior perception and analysis for recognizing an image object with a random mode as a feature, and the method comprises the following steps: obtaining a foam image sequence in a target area, and collecting the foam image sequence through imaging equipment, the image frames of the foam image sequence have time continuity; and performing disturbance feature extraction operation on the foam image sequence to obtain local disturbance speed information, membrane surface tension change trend information and form boundary fluctuation information of the foam edge within a preset time. According to the method, foam structure disturbance characteristics are extracted from an image time sequence, a structure evolution graph memory bank is constructed, and irregular sudden change image behaviors are identified in combination with a trend matching mechanism, so that dynamic perception and abnormal response of a sudden foam state without prior support are realized, and the problem that a random mode foam state cannot be identified is solved.

Systems and methods are provided for image guided surgery. Such systems and methods receive a model of an anatomical structure as well as a model of an object, and process a projection image generated by an imaging device from an imaging direction, wherein the projection image includes at least a part of the anatomical structure and at least a part of the object. Based on (i) the projection image, (ii) the imaging direction, (iii) the model of the object, and (iv) the model of the anatomical structure, the systems and methods determine a spatial position and orientation of the object relative to a space of movement. The space of movement may be defined in relation to the anatomical structure.

The invention discloses an intelligent welding quality evaluation method and system based on vision, and relates to the technical field of welding quality evaluation.The method comprises the steps that the welding process is monitored in real time through multi-mode imaging equipment, weld joint surface texture, molten pool heat distribution and three-dimensional shape information are synchronously obtained, and a three-dimensional voxel model is constructed; establishing a two-stage multi-channel architecture for executing dynamic resource scheduling, and extracting geometric features and deep semantic features in the three-dimensional voxel model; and on the basis of the defect confidence coefficient vector, in combination with the real-time process parameters and the molten pool state, a dynamic evaluation model of the welding quality index is constructed, and time-phased quality scores are calculated and output through stage weighting. According to the method, the welding seam surface texture, molten pool heat distribution and three-dimensional shape information are synchronously collected through multi-mode imaging equipment, a texture-temperature-space coordinate bound three-dimensional voxel model is constructed, and multi-source heterogeneous data fusion analysis of the welding quality is achieved.

Systems and methods for performing a medical imaging analysis task from PCCT (photon counting computed tomography) imaging data are provided. PCCT imaging data acquired from a PCCT imaging device is received. A plurality of PCCT virtual images is generated from the PCCT imaging data. A plurality of medical imaging analysis sub-tasks is performed based on the plurality of PCCT virtual images using a plurality of machine learning based networks. Results of the medical imaging analysis sub-tasks are combined to generate results of a medical imaging analysis task. The results of the medical imaging analysis task are output.

An image-forming apparatus includes a housing, a fixing device, a shutter, a link, a circuit board, and a fan. The fixing device includes a heating unit and is configured to thermally fix a toner image to a sheet conveyed in a conveying direction. The shutter is movable relative to the fixing device between a closing position for closing an upstream end of the heating unit in the conveying direction and an opening position for exposing the upstream end of the heating unit. The link is connected to the shutter, and is configured to abut on a cartridge, which is detachably attachable to the housing, to move the shutter between the closing position and the opening position. The circuit board and the fan are spaced apart from each other to provide an airflow path through which air flows. The link is positioned between the circuit board and the fan.

The invention relates to the technical field of image data processing, and discloses a fine metal powder defect detection method and system based on image recognition, and the method comprises the steps: illuminating a metal powder region through a non-polarized light source, and collecting two images in an orthogonal polarization state through imaging equipment with a linear polarizer, according to the method, the defect morphological fingerprints are constructed through the polarization information entropy, the three-dimensional structure information is converted into light field order degree change for detection, the dimension limitation of traditional two-dimensional imaging is broken through, and the detection accuracy is improved. Hidden defects such as internal pores and aggregates can be accurately identified, and meanwhile, stable detection and material defect cross discrimination under a dynamic working condition are realized in combination with motion compensation and multiband entropy ratio verification.

The invention discloses a fan blade inner cavity defect detection method and system based on image recognition and deep learning. The method comprises the following steps: performing multi-angle and multi-position image acquisition on an inner cavity of a fan blade through imaging equipment; the collected fan blade inner cavity image data set is preprocessed; generating a primary defect sample by using the first-layer generative adversarial network model; generating a complex defect image by using the second-layer generative adversarial network model; a convolutional neural network model is used for training; inputting the preprocessed fan blade inner cavity image data set into the trained convolutional neural network model; generating a defect report based on a defect detection result; when a new defect type appears, the recognition capability of the new defect is continuously optimized. Compared with the existing single detection technology, the multi-network combination method disclosed by the invention has the advantage that the defect detection efficiency and the classification capability are remarkably improved.

The invention discloses a multi-source image data fusion-based fine decoration air crack seepage quality problem detection method and device, and solves the technical problem of how to carry out comprehensive, high-precision and intelligent detection on the fine decoration surface air crack seepage quality problem. Comprising the following steps: 1) receiving visible light image data, thermal infrared image data and three-dimensional laser point cloud data of a target refined decoration surface acquired from visible light acquisition equipment, thermal infrared imaging equipment and three-dimensional laser scanning equipment respectively; 2) carrying out feature extraction on the visible light image data, the thermal infrared image data and the three-dimensional laser point cloud data; 3) obtaining point cloud projection coordinates, and then performing association fusion on the first two-dimensional feature and the second two-dimensional feature with corresponding three-dimensional features to generate fusion point cloud data containing multi-source features; and 4) based on the fused point cloud data, carrying out defect classification identification and spatial positioning to obtain a detection result. And comprehensive and high-precision detection of the quality problem of air crack seepage of the finely-decorated surface is realized.