5793 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.

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.

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.

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.

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 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.

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.

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.

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 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.

An image-forming apparatus includes a housing, a cartridge, a fixing device, a shutter, a link connected to the shutter, and an electrical contact. The fixing device includes a heating unit. The shutter is movable between a closing position for covering an upstream end of the heating unit in a sheet conveying direction and an opening position for uncovering the upstream end of the heating unit. The link is provided on the housing and is configured to abut on the cartridge to move the shutter between the closing position and the opening position. The electrical contact is provided on the housing and contacts an electrical contact surface of a memory of the cartridge attached to the housing. In the housing, the link and the electrical contact are positioned on opposite sides of a center line of the attached cartridge in a widthwise direction orthogonal to an attachment / detachment direction of the cartridge.

The invention discloses a laser powder bed forming defect identification method based on mask segmentation optimization. The method comprises the following steps: S1, acquiring image data of a forming area in a laser powder bed forming process in real time by using imaging equipment; s2, obtaining preprocessed image data; s3, extracting key features in the image based on the preprocessed image data by adopting a multi-scale feature extraction method; s4, generating a corresponding defect area mask; s5, performing feature extraction on the defect area according to the generated defect area mask; and S6, classifying and evaluating the extracted defect region features, determining the type and severity of the defect, and forming a defect identification result. According to the method, the texture features are calculated through the gray-level co-occurrence matrix, the edge features are extracted through Canny edge detection, and the defect types are comprehensively judged in combination with regional morphological analysis, so that the recognition accuracy of different defects is improved.

The invention discloses an automobile part surface defect detection method based on a large model. The method comprises the following steps: acquiring feature data of a target surface by using a multi-dimensional imaging device; feature extraction, clustering analysis and abnormal mode recognition are carried out through the large model; extracting an abnormal region and generating a statistical model; comparing the statistical model with standard sample data to determine the position of an abnormal region; defect types are classified and identified, and resource allocation is dynamically optimized and calculated to adapt to detection complexity; and finally outputting defect types and positions. According to the method, the spectral imaging technology and the deep learning algorithm are combined, high-precision detection, positioning and classification of defects such as cracks, scratches and bubbles are achieved, a visual detection report is generated, the method is suitable for real-time quality control in large-scale production, the detection efficiency and accuracy are improved, and manual errors and cost are reduced.

A method for identifying a log file for resolution of a fault of a medical imaging device, is provided. The method includes: obtaining log file browsing data describing one or more log files of the medical imaging device already viewed by a user to resolve the fault of the medical imaging device; obtaining problem data describing the fault of the medical imaging device; inputting the problem data and the log file browsing data to a machine learning algorithm, the machine learning algorithm being trained to predict, for each of a plurality of log files of the medical imaging device, and based on the browsing data, a resolution probability indicating a likelihood that the log file will assist in resolution of the fault of the medical imaging device; obtaining a prediction result from the machine learning algorithm in response to the inputting, the prediction result comprising a resolution probability for one or more of the plurality of log files of the medical imaging device; and identifying a log file for resolution of the fault of the medical imaging device based on the obtained prediction result.

The invention provides a heading machine positioning method and system, and relates to the technical field of heading machines, and the method comprises the steps: pre-arranging a target on a heading machine, and building a heading machine gravity center coordinate system; capturing a plurality of frames of images with targets by adopting imaging equipment; performing image recognition on the multi-frame image by adopting a target detection model, extracting a feature point set of an area where the target is located, and generating multi-frame data of each feature point in the feature point set; performing multi-angle fusion on each feature point based on the multi-frame data of each feature point, and converting each feature point from a two-dimensional coordinate in an image coordinate system to a three-dimensional coordinate in a world coordinate system; based on the centroid coordinate system of the heading machine, converting the three-dimensional coordinates of the feature points in the world coordinate system into the three-dimensional coordinates in the centroid coordinate system of the heading machine, so as to generate position information of the heading machine; the problem that an existing heading machine is poor in pose positioning result accuracy is solved.

Systems and methods are provided for enabling quick interactions with a user's frequent contacts from a wrist-wearable device. The method includes displaying, at a wrist-wearable device, a user interface including a plurality of representations of contacts. The plurality of representations of contacts includes a first representation of a first contact that is associated with an image-sharing application, and a second representation of a second contact that is associated with a fitness application that allows a user of the wrist-wearable device to compare performance of a physical activity against at least the second contact. The method includes, responsive to an input selecting the first representation of the first contact, causing activation of an imaging device for capturing an image. The method includes responsive to another input, capturing an image using the imaging device and causing the image to be sent to the first contact via the image-sharing application.

Disclosed in the present invention is a soft tissue photoacoustic / ultrasonic multi-modal image fusion method based on deep learning. The method comprises the following steps: an ultrasonic-photoacoustic imaging device collecting a human body soft tissue photoacoustic image and ultrasonic image, and performing size normalization processing; inputting the images into a spatial conversion module to convert same into a YCbCr space; inputting the images into a pre-convolutional module to change the number of data channels; inputting the images into a multi-scale feature extraction module to extract salient features of the source images; inputting the features into a filter prediction module to obtain a multi-scale filter; and inputting the filter into a filtering fusion and adaptive enhancement module and combining same with the input source images, so as to obtain a final fusion result. Compared with several traditional fusion methods and deep-learning-based fusion methods, the method provided in the present invention has a greater fusion effect, and more importantly, has an excellent real-time performance. Moreover, multiple modes of photoacoustic / ultrasonic fusion extension experiments are performed on a photoacoustic / ultrasonic multi-modal imaging system, verifying the effectiveness of the method in the present invention.

The invention relates to the technical field of super-resolution reconstruction, and provides an infrared image super-resolution reconstruction method based on noise decoupling, which comprises the following steps: S1, data input and preprocessing: down-sampling a high-resolution image to 64 * 64 or 32 * 32 low-resolution input through bicubic interpolation by adopting an FLIR infrared data set, and normalizing a pixel value to [0, 0]; the method comprises the following steps: 1) taking a low-resolution infrared image ILR adaptive to a neural network as subsequent input, and carrying out noise decoupling on the ILR; the method has remarkable advantages in the infrared image super-resolution reconstruction task, the details of the infrared image can be effectively recovered, and the resolution and quality of the reconstructed image are improved; the method is suitable for infrared image super-resolution reconstruction tasks of different imaging devices and noise levels, and especially has important application value in the fields of medical imaging, environment detection and the like with high image resolution requirements.

Embodiments of the present disclosure provide a medical system and method. The medical system includes an equipment room, a plurality of medical rooms, the imaging device, and a plurality of shielding doors. The imaging device includes the movable imaging component. The movable imaging component is shared by the plurality of medical rooms, and the movable imaging component is configured to move between the equipment room and the plurality of medical rooms. For each of the plurality of shielding doors, the shielding door is provided between one of the plurality of medical rooms and the equipment room.

An imaging device includes an imaging element unit that is exposed to incident light and that outputs a captured image signal, a notification unit, and a control unit that performs control in such a way as to cause the notification unit to perform notification corresponding to still image capture exposure, which is performed by the imaging element unit to generate a still image in response to a still image capture instruction, in a period different from an exposure period of the still image capture exposure.

Provided in the present invention are an imaging device and method for turbid oil abrasive particles based on polarized image enhancement, which belong to the technical field of image processing. The imaging device of the present invention includes a CMOS fixing device, a Stokes polarized CMOS sensor, an optical lens sleeve and an optical magnifying glass, where the Stokes polarized CMOS sensor includes a CMOS sensor and a Stokes analyzer. By using a polarization characteristic difference between scattered light of lubricating oil media and reflected light of abrasive particles, Stokes vector information is measured in real time under turbid lubricating oil scenes, the scattered light of the media is filtered out, image quality of the abrasive particles is improved, coverage duration for monitoring abrasive particle imaging is effectively prolonged.

The invention relates to the technical field of display panel manufacturing, in particular to a liquid crystal module hot-pressing binding method based on intelligent temperature control, which comprises the following steps: step 1, hot-pressing area partitioning and dynamic temperature calibration: dividing a contact surface of a hot-pressing head into a plurality of independently controlled temperature areas, each area is provided with at least one temperature sensor, and the temperature sensors in all the areas are calibrated through thermal imaging equipment, so that the deviation between the sensors and the real temperature is eliminated; 2, dynamic temperature compensation and pressure cooperative control are carried out; step 3, iteratively optimizing hot pressing process parameters, and training the deep learning model based on historical binding data to predict an optimal temperature-pressure parameter combination of a next binding period; and automatically correcting the initial temperature threshold in the step 1 and the target pressure in the step 2 according to a prediction result. Through the intelligent temperature control and pressure cooperative control technology, the temperature and pressure in the hot pressing process of the liquid crystal module are accurately adjusted, and the bonding quality and the production efficiency are remarkably improved.

According to the intelligent imaging generation system and method, parameters of a spraying mechanical arm and imaging equipment are adjusted in a linkage mode in real time through a dynamic parameter adjusting module, and closed-loop control over the spraying quality is achieved in combination with a defect prediction and optimization decision module and a multi-modal process knowledge base. A high-resolution visual sensor, a capacitive thickness detector and a temperature and humidity sensor are integrated in the system, multi-source data are fused through a Kalman filtering algorithm, and the mechanical arm speed, the nozzle pressure and the imaging gain coefficient are dynamically adjusted. The defect prediction module simulates a coating defect form based on a physical constraint generative adversarial network and generates a process optimization instruction set through reinforcement learning. The multi-modal process knowledge base stores a correlation model of material characteristics, environmental data and defect modes, and supports incremental learning and historical data attenuation updating. And through dynamic parameter collaborative optimization, defect prediction preposition and multi-modal data fusion, the spraying quality detection efficiency and the coating consistency are improved, and the rework rate is reduced.

The invention provides an optical imaging lens and imaging equipment, and belongs to the technical field of optical imaging. The optical imaging lens comprises a first lens with negative focal power, a second lens with positive focal power, a diaphragm, a third lens with negative focal power, a fourth lens with positive focal power, a fifth lens with positive focal power, a sixth lens with negative focal power, a seventh lens with positive focal power and an eighth lens with negative focal power which are sequentially arranged in the direction of an optical axis. The image side surface of the first lens is a concave surface, the Abbe number of the fourth lens is greater than that of the third lens, the object side surface and the image side surface of the fifth lens are rotationally symmetrical aspheric surfaces, the object side surface of the sixth lens is a concave surface, and the Abbe number of the seventh lens is greater than that of the sixth lens; the object side surface and the image side surface of the eighth lens are rotationally symmetrical aspheric surfaces and are respectively a concave surface and a convex surface, and the optical imaging lens meets set optical parameter conditions. According to the invention, the requirements of miniaturization, large wide angle, large aperture and high imaging quality can be met.