2589 results about "Imaging equipment" patented technology

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.

The present invention provides an automatic focusing method, an electronic device, and a readable storage medium. The automatic focusing method comprises: using a target detection model to detect a target object in an image frame acquired at an initial focal length by an optical imaging device to be focused, so as to acquire a target object detection result; on the basis of the target object detection result, determining whether a target object is present in the image frame; if yes, determining an actual object distance on the basis of the target object detection result and the initial focal length, and on the basis of the actual object distance and a mapping relationship between the object distance and an optimal imaging focal length, determining the optimal imaging focal length; and if not, using a preset search algorithm to search for a focal length until an image having the highest definition value is found, and using a focal length corresponding to the image having the highest definition value as the optimal imaging focal length. The present invention primarily employs a deep learning-based automatic focusing method, supplemented by an image definition evaluation method. Compared with traditional passive focusing methods, the present invention greatly improves the focusing efficiency. Compared with traditional active focusing methods, the present invention eliminates a need for adding an additional optical ranging component, ensuring that the imaging device has a simple structure and a low cost.

The invention, which belongs to the technical field of artificial intelligence, discloses a multi-modal cross-view-angle air-ground cooperation big model system comprising a heterogeneous agent module, a multi-modal data processing module, a cross-view-angle fusion module, a big language model reasoning module and an action decision module. The heterogeneous intelligent agent module comprises an unmanned aerial vehicle, a ground robot and an unmanned vehicle, the unmanned aerial vehicle is provided with a high-resolution camera, an infrared imaging device, a laser radar and a meteorological sensor, the ground robot is provided with the laser radar, a camera and a force sensor, and the unmanned vehicle is provided with a camera, a radar and an inertial measurement unit. The multi-view features are fused through a cross attention mechanism, precise mapping from the features to real coordinates is realized in combination with a ScaleToRealClip model, and scale and coordinate differences of air and ground data are eliminated; meanwhile, the cross-view fusion module can dynamically update environment coordinates, help an intelligent agent to perceive temporary obstacles in real time, and construct a comprehensive and accurate environment cognition model.

The invention belongs to the technical field of image processing, and particularly relates to a casting surface defect detection method and system based on image segmentation, and the method comprises the steps: obtaining a gray-scale image and a brightness background image of a vortex casting to be detected, and calculating the illumination deviation of each position in the image; segmenting the grey-scale map of the vortex casting to be detected by using an Otsu method to obtain a binary image; constructing a circular structural element according to the width of the optical interference region and the resolution of the imaging equipment; performing corrosion operation on the binary image based on the structural elements to generate a structural saliency image; obtaining a defect saliency value by using a product of a gray value and an illumination deviation in the structure saliency map; and calculating a segmentation threshold value based on the statistical characteristics of the defect-free sample, and identifying the position of which the defect saliency value is greater than the segmentation threshold value as a defect point. Through physical constraint and illumination feature fusion, the problem that a dark vortex structure and a real defect are difficult to distinguish is effectively solved, and the detection precision is improved.

The invention discloses an image super-resolution reconstruction method and system based on a deep learning model, and the method comprises the steps: collecting a preset number of high-resolution images, and obtaining a degradation phenomenon which may occur during the imaging of a target imaging device; degenerating the high-resolution image according to a preset rule on the basis of a degeneration phenomenon possibly occurring during imaging of the target imaging equipment to obtain a corresponding low-resolution image; establishing a training data set according to the high-resolution image and the corresponding low-resolution image, constructing a preset neural network and a loss function, and inputting the training data set into the preset neural network for training until the loss tends to be stable to obtain an image super-resolution reconstruction model; and obtaining a to-be-reconstructed low-resolution image, and inputting the to-be-reconstructed low-resolution image into the corresponding image super-resolution reconstruction model to obtain a corresponding reconstructed high-resolution image. The low-resolution image generated by the method is closer to real degradation distribution, so that the quality of super-resolution reconstruction of the image is improved.

An agricultural system includes one or more processors and memory storing instructions executable by the one or more processors. The instructions, when executed by the one or more processors, configure the agricultural system to: obtain an image captured by an imaging device during an agricultural operation performed by an agricultural work machine, the image indicating a characteristic; segment the image to generate a segmented image having a plurality of cells and selectively size each cell of the plurality of cells according to one or more cell size criteria; determine one or more values of the characteristic based on the segmented image; and control an operating parameter of the agricultural work machine based, at least, on the one or more values of the characteristic.

Provided is an information processing apparatus including a processor; and a memory that is connected to or incorporated in the processor, in which the processor acquires imaging device position information regarding positions of a plurality of imaging devices and imaging direction information regarding an imaging direction of each of the plurality of imaging devices, derives virtual viewpoint range specification information capable of specifying a virtual viewpoint range in which a virtual viewpoint image is capable of being generated on the basis of a plurality of captured images obtained by being captured by the plurality of imaging devices, on the basis of the imaging device position information and the imaging direction information, and outputs information regarding the virtual viewpoint range specification information.

An endoscopic imaging system for use in a light deficient environment includes an imaging device having a tube, one or more image sensors, and a lens assembly including at least one optical elements that corresponds to the one or more image sensors. The endoscopic system includes a display for a user to visualize a scene and an image signal processing controller. The endoscopic system includes a light engine having an illumination source generating one or more pulses of electromagnetic radiation and a lumen transmitting one or more pulses of electromagnetic radiation to a distal tip of an endoscope.

The invention provides a maglev train bottom bolt looseness identification method based on deep learning. The method comprises the following steps: acquiring a bottom image of a maglev train by adopting imaging equipment, and carrying out bolt target detection on an image in a bottom image data set through an improved YOLOv8 network to obtain a bolt image; performing classification processing on the bolt image through a lightweight network RepViT fused with a lightweight ViT design principle; color space conversion, image binaryzation, morphological processing and connected domain screening processing are carried out on the bolt image to extract an anti-loosening line, and whether the vehicle bottom bolt in the bolt image is loosened or not is judged based on the bolt anti-loosening line according to loosening characteristics of different types of bolts under multiple visual angles. According to the method, the detection precision is improved by means of improving a target detection network structure, refining bolt loosening characteristics, providing adaptive detection methods for different types of bolts and the like; by designing a lightweight classification network mode, the calculation amount and time consumption are reduced, and the detection efficiency is improved.

The invention discloses a railway freight car loading state detection method based on image recognition, and particularly relates to the field of railway freight car loading state detection.The method comprises the steps that in the process that a railway freight car passes through an image collection area, carriage image frames are collected at a constant frame rate through linear array imaging equipment; an image sequence is constructed based on the wheel axle displacement, the acquisition timestamp and the frame number, the image sequence comprises a main image frame, a relay interpolation frame and an interpolation pseudo frame, and the main image frame, the relay interpolation frame and the interpolation pseudo frame are arranged to support time domain modeling of subsequent state response changes. The dynamic capture and accurate identification of the instantaneous abnormal state of the loading structure are realized by constructing an anchor point tracking graph and combining a time sequence rhythm modeling and similarity discrimination mechanism of a disturbance behavior. The problems that in the prior art, an identification system lacks the space-time continuous modeling capacity for the short-time change state, and an abnormal judgment blind area and misjudgment distortion exist are solved.

The invention relates to a cone beam CT imaging scanning parameter determination method and imaging equipment, and the method comprises the steps: designing an optimization model for a microgravity environment, carrying out the optimization processing of the cone beam CT imaging scanning parameters based on a pre-constructed optimization model with the maximum imaging performance as a target, and obtaining the optimal parameters of the cone beam CT imaging. Wherein optimization problem modeling and solving are sequentially carried out in a projection domain and an image domain so as to obtain target scanning parameters meeting constraint conditions, and according to the technical scheme of the invention, aiming at factors such as scanning space and energy consumption in space, requirements of high spatial resolution, compact physical size of equipment, high image quality and low scanning dose are met, and the method and the device have the advantages of being high in efficiency and high in reliability. The method is applied to cone beam CT imaging in the space microgravity environment, and the comprehensive requirements of scientific research, health guarantee and medical service in the space environment are met.

The present invention relates to novel image reconstruction methods and technologies. Embodiments of the invention specifically relate to image reconstruction using a flat lensless camera, or any other imaging device and / or imaging system, and a pre-trained diffusion model with a control network and a learned separable transformation. Additional embodiments of the invention include the addition of textual descriptions and / or text captions of the captured scene to further enhance image reconstruction.

Disclosed is a computer-implemented method which encompasses registering a tracked imaging device such as a microscope having a known viewing direction and an atlas to a patient space so that a transformation can be established between the atlas space and the reference system for defining positions in images of an anatomical structure of the patient. Labels are associated with certain constituents of the images and are input into a learning algorithm such as a machine learning algorithm, for example a convolutional neural network, together with the medical images and an anatomical vector and for example also the atlas to train the learning algorithm for automatic segmentation of patient images generated with the tracked imaging device. The trained learning algorithm then allows for efficient segmentation and / or labelling of patient images without having to register the patient images to the atlas each time, thereby saving on computational effort.

An illustrative system may access a first image sequence captured by an imaging device during a medical procedure, the first image sequence comprising first images. the first images based on illumination of a scene associated with the medical procedure using visible-spectrum light: access a second image sequence captured by the imaging device during the medical procedure. the second image sequence comprising second images. the second images based on illumination of the scene using non-visible spectrum light: and provide the first image sequence and the second image sequence to a machine learning module.

A computer-implemented method for comparing medical images may include obtaining image data produced by multiple imaging devices indicative of anatomical features associated with an arthroplasty procedure, preprocessing the image data, including projecting three dimensional image data produced by a first imaging device of the multiple imaging devices to a plane represented by two dimensional image data produced by a second imaging device of the multiple imaging devices, identifying one or more landmarks associated with the anatomical features for comparison between images in the image data, comparing the identified one or more landmarks across the images to determine whether the images represent different individuals, and producing a notification indicative of a result of the determination of whether the images represent different individuals.

A method for designing and optimizing a lensless imaging device includes: (a) a method for optimizing the point spread function for various imaging, computer vision and artificial intelligence tasks, (b) a method for computing the optimal phase mask that can realize the desired point spread function, (c) using the optimal phase mask in a lensless camera and (d) a method for calibrating the lensless imaging device using a single captured image.

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.

A system includes proximal and distal structures and a processor system. The processor system determines a first commanded motion of the proximal structure; determine a second commanded motion for joint(s) of the distal structure or an imaging device, wherein when the first and second commanded motions are performed in conjunction, a field of view of the imaging device is maintained relative to a workspace; and in response to determining that driving the joint(s) in accordance with the second commanded motion would cause the distal structure or the imaging device to violate a constraint: determines an alternate commanded motion for the joint(s) to maintain a defined geometric relationship between a first geometric feature fixed relative to the imaging device and a second geometric feature fixed relative to the workspace, drives the proximal structure in accordance with the first commanded motion, and drives the joint(s) in accordance with the alternate commanded motion.

To provide an information processing method, an information processing device, and a computer program. [Solution] A computer executes a process of acquiring image data captured by an imaging device and measurement data measured by a measuring device communicatively connected to the imaging device, and storing the acquired measurement data in association with information about the measurement object identified based on the image data.

A point-of-sale terminal attachable to a shopping cart, includes an interface connected to an imaging device, an input device, and a processor configured to: control the imaging device to capture an image and generate image data based on the captured image, perform object recognition on the image data, and perform an automatic registration process for automatically registering an item when the item is identified by the recognition. The processor is configured to: determine whether a predetermined motion of a body part is detected based on a result of recognition or whether a predetermined operation is input via the input device, upon determining that the predetermined motion is detected or the predetermined operation is input, temporarily disable the registration process such that an item is not automatically registered for purchase or automatically removed from a list of registered items even when the item is identified by the recognition.

The invention discloses an aluminum profile dynamic extrusion control system and method, which can solve the problems that the internal temperature of an aluminum bar is difficult to accurately obtain in the traditional extrusion technology, and the speed and temperature regulation are disjointed, the system comprises an image acquisition module, a thermal imaging device is used for obtaining a thermal distribution diagram after the aluminum bar is heated and cooled and separated, and a shallow layer characteristic diagram is generated; the temperature change data analysis module calculates the temperature change rate; the internal temperature modeling module calculates the internal actual temperature by combining the specification of an aluminum bar and the like, the internal temperature verification module is matched to ensure the precision, and the heating strategy generation module generates an optimal heating strategy by means of machine learning. The extrusion speed regulation and control module dynamically corrects the extrusion speed; the temperature field optimization module adjusts process parameters through simulation; the monitoring feedback module monitors and processes exceptions in real time; the method has the advantages that accurate sensing of the internal temperature of the aluminum bar and full-process dynamic cooperative regulation and control can be achieved, the product quality stability and the production efficiency are improved, and energy consumption is reduced.

This disclosure provides an X-ray imaging device and its power distribution unit and wireless power transmission device. The power distribution unit includes: a first rectifier circuit, a charging circuit, an energy storage module, and a first inverter circuit; the input terminals of both the first rectifier circuit and the charging circuit are connected to the external power grid; the output terminal of the first rectifier circuit is connected to the input terminal of the first inverter circuit, and the output terminal of the first inverter circuit is connected to the wireless power transmission device of the X-ray imaging device; the output terminal of the charging circuit is connected to one end of the energy storage module, and the other end of the energy storage module is connected to the input terminal of the first inverter circuit. In this disclosure, the power supply from the power grid and the power supply from the energy storage module are in parallel, which can reduce the imaging device's demand on the power grid capacity. Since the charging circuit only participates in the charging process of the energy storage module and does not participate in the discharging process of the energy storage module, it can realize simultaneous charging and discharging of the energy storage module, and there are no excessive requirements on the power level of the charging circuit, which can reduce the cost of the charging circuit.

The invention provides an electrical equipment defect identification method and system based on machine vision, and relates to the technical field of equipment defect identification, and the method comprises the steps: collecting and preprocessing a standard electroluminescent image, a standard infrared thermal image and a standard visible light image of a target photovoltaic string under different time windows through an imaging device carried by an unmanned aerial vehicle, and carrying out the collection of the standard electroluminescent image, the standard infrared thermal image and the standard visible light image; meanwhile, recording environmental parameters collected each time; analyzing visual defect features and attenuation gradient features in the standard electroluminescent image, and performing fusion verification by combining the standard infrared thermal image, the standard visible light image and the electrical performance data to obtain definite judgment of potential-induced attenuation; for the photovoltaic module which is diagnosed and judged to have potential-induced degradation, based on the environmental parameters, evaluating by using a time sequence analysis method to obtain the evolution rate of the potential-induced degradation, and mapping to generate the risk level of the potential-induced degradation; and generating a structured defect identification report. And the accuracy of defect identification of the power equipment is improved.

The invention discloses a steel structural member local corrosion intelligent identification and evaluation method based on deep learning, and the method comprises the steps: obtaining a steel structure surface image through an imaging device, carrying out the feature extraction and corrosion region identification of an input image through a YOLOv12 semantic segmentation model which introduces a CBAM self-attention mechanism, and generating a pixel-level corrosion mask; the actual physical area of the corrosion area is automatically calculated based on an area calculation algorithm of conversion of mask integration and ground sampling distance; the residual bearing performance of the corroded component is evaluated in combination with the corrosion area ratio and the morphological characteristic prediction model; and finally realizing visual display and report output of detection results, area data and performance indexes through a graphical interface. According to the invention, full-process automatic processing of local corrosion of the steel structural member from detection, segmentation, quantitative measurement and calculation to member performance evaluation is realized, and closed-loop intelligent evaluation from visual detection to mechanical evaluation is realized.

Provided is a buffer circuit that can realize a wide dynamic range, a reduced area, and a reduced power consumption amount while maintaining low output impedance. The buffer circuit includes a first transistor, a current source, a second transistor, an output terminal, and first and second capacitors. The first transistor has a gate to which an input signal is input. The current source is connected to one terminal of the first transistor. The second transistor is connected to the other terminal of the first transistor. The output terminal is connected to the one terminal or the other terminal of the first transistor. The first and second capacitors are between the current source and the gate of the second transistor. The first transistor, the second transistor, and the first capacitor form a first feedback circuit. The first transistor, the current source, and the second capacitor form a second feedback circuit.

To make it easier for safety managers to check the site conditions. [Solution] The safety support system 100 includes an object detection unit 13 that detects moving images of objects that meet detection conditions from among multiple video sources MI10 of each detection area 200 captured by multiple imaging devices (cameras 110) registered in a database 30; an alarm condition setting unit 12 that registers detection conditions; a detection moving image extraction unit 17 that extracts and saves moving images of objects that meet detection conditions as detection moving images MI11 from among the video sources detected by the detection conditions; and an extracted moving image viewing unit 18 that displays multiple detection moving images of the same detection area on a display device 3a in synchronization with the time of imaging.

An isolation ratio between a plurality of sub-pixels included in a pixel is further improved. An imaging device including a semiconductor substrate including a photoelectric conversion unit provided for each of pixels arranged two-dimensionally and a pixel isolation unit that isolates the photoelectric conversion units from each other, a color filter and an on-chip lens provided for each of the pixels on one surface of the semiconductor substrate, an inter-filter isolation unit provided to include a low refractive index material having a refractive index lower than a refractive index of the color filter between the color filters and isolate the color filter for each of the pixels, and a sub-pixel isolation unit that isolates the photoelectric conversion units of the pixels including a plurality of sub-pixels for each of the sub-pixels.

Systems and methods directed to image rectification are provided. The method may include capturing, by a pair of imaging devices, a pair of live images of a scene. The method may further include creating a combined image based on the image pair and alignment parameters. The method may further include identifying a first feature in the first image and a second feature in the second image. The method may further include calculating a spatial deviation based on the first feature and the second feature. The method may include identifying whether the spatial deviation exceeds a predetermined threshold, wherein, if so, then the method may include generating rectification parameters based at least on the spatial deviation. Additional systems and methods are also provided.

The invention provides a low-altitude unmanned aerial vehicle visual target tracking method based on dynamic sparse Sinkhorn attention, and belongs to the technical field of unmanned aerial vehicle intelligent perception. According to the method, an unmanned aerial vehicle carries imaging equipment to obtain video images of a target template area and a search area, and visual and language tokens are respectively generated in combination with natural language description; a dynamic sparse Sinkhorn attention module is embedded behind a multi-head self-attention layer of a visual encoder, an approximate double-random transmission matrix is constructed through similarity calculation and iteration, and differential sorting between tokens is achieved; a previous visual token most related to target semantics is dynamically screened and reserved according to the correlation score, background interference and redundant information are inhibited, and airborne calculation burden is reduced; and fusing the screened tokens in a prediction network, and outputting a target existence probability and a bounding box to realize stable real-time tracking. According to the method, the calculation complexity is reduced on the premise of ensuring the precision, and a low-power-consumption embedded platform is adapted; the robustness in a complex low-altitude environment is enhanced; and the cross-domain generalization ability is improved.

A surgical navigation system is provided to create a plan to correct a deformed spinal alignment. A processor is configured to obtain a first set of image data associated with a deformed alignment in a spine of a patient from at least one imaging device. The processor is also configured to process the first set of image data to identify a set of deformed alignment parameters associated with the deformed alignment. The processor is further configured to identify a set of corrected alignment parameters. The processor is also configured to process the first set of image data, the set of deformed alignment parameters, and the set of corrected alignment parameters to generate a correction plan to surgically manipulate the deformed alignment to the preferred alignment. The processor is additionally configured to provide navigation through the correction plan to facilitate surgical manipulation of a patient spine to the preferred alignment. The processor is also configured to receive information relating to forces on a rod-link reducer or surgical implants from strain gauges to aid the correction plan.