148 results about "Imagery analysis" patented technology

The invention provides a crop yield prediction method and system based on image analysis, relates to the field of yield prediction, and obtains a farmland multi-view remote sensing image through an unmanned aerial vehicle multispectral sensor, and executes aerial triangulation and ortho-rectification to obtain accurate exterior orientation elements and ortho-images. Then, vegetation indexes are extracted and combined with field yield measurement data, a yield regression model is constructed, and rapid prediction and grading of the crop yield are achieved. In order to reduce multi-view brightness inconsistency and give consideration to crop growth characteristics, prior information such as canopy row direction, leaf angle distribution and sealing degree is introduced, iterative correction is performed on the image by adopting a BRDF model, and radiation difference is corrected while geometric accuracy is guaranteed. Particularly, through staged trend analysis and multiple rounds of detection, priori conflicts can be found in time, incremental images can be flexibly collected, and resolving robustness and yield prediction precision are improved. The method can be widely applied to unmanned aerial vehicle remote sensing agricultural monitoring scenes.

The invention relates to the technical field of bird recognition, in particular to an infrared target image multi-target recognition method based on image analysis. According to the flight vector of the bird area in each frame of the environment infrared image, carrying out optical flow tracking on pixel points of the bird area in each frame of the environment infrared image, and according to the pixel point position distribution of the bird area in each frame of the environment infrared image and the consistent condition of the bird area and the movement direction of the pixel points, adjusting the movement condition of the pixel points in the bird area. And acquiring the flight distance of the bird area in each frame of the environment infrared image, and determining the risk degree of the bird area in each frame of the environment infrared image by combining the distance between the bird area and the facility area in the direction of the flight vector. According to the invention, the optical flow change caused by the wing movement is accurately adjusted in different flight states, so that the overall flight analysis of the bird is more accurate, and the accuracy of the judgment of the danger degree of the bird is improved.

The invention relates to the field of automobile electronic testing technology and equipment, and discloses a vehicle-mounted camera imaging quality testing method and device suitable for a rain and fog scene, and the method comprises the steps: installing a to-be-tested camera module in a rain and fog environment cabin, setting the spectrum and illumination of a top natural illumination simulation system, and simulating the illumination at different time periods; selecting a test target plate in the darkroom; starting an automatic centering module to align the optical axis with the standard plate reference; rain and fog parameters are set through the environment control host, the rain and fog supply system is started to simulate different levels of rain and fog environments, and the raindrop particle size, concentration, dripping speed, fog visibility and fogdrop particle size are dynamically regulated and controlled; shooting a target plate graph card, extracting characteristic parameters by an image analysis module, performing quantitative analysis, and generating an evaluation report, so as to solve the technical problem of singleness of rain and fog scene environment simulation in the prior art.

The invention belongs to the technical field of soil detection, and discloses a garden soil quality detection system and method based on environmental monitoring, and the method comprises the steps: a fixed-point data collection module carries out fixed-point monitoring; the route planning module plans an optimal flight route and a stop point of the unmanned aerial vehicle; the inspection module is used for performing all-around soil quality inspection on the garden; the image analysis module, the spectral data analysis module and the radar data analysis module identify conditions and abnormal conditions which need to be subjected to soil sampling analysis; the comprehensive evaluation module comprehensively evaluates the soil; the risk prediction module fuses multi-source data and predicts the potential risk of the garden in combination with historical data. According to the invention, through a mode of combining fixed-point detection and routing inspection detection, all-around soil quality detection is carried out on the garden; the sampling flexibility and reliability are guaranteed through the design of the sampling assembly, and the comprehensive evaluation module fuses multi-source data and comprehensively and objectively evaluates the soil quality; the risk prediction module predicts potential risks in combination with historical data.

The system comprises a base, a power distribution box and a three-dimensional holder are arranged on the base, an infrared thermal imaging module and a camera are installed at the mobile end of the three-dimensional holder, and the power distribution box is provided with a Bluetooth module, a controller and a driver. The infrared thermal imaging module and the camera are respectively connected with the input end of the controller through network cables, the output end of the controller is connected and communicated with the computer through a network cable, the controller is connected with the three-dimensional holder through the driver, and the controller is connected with the IMU sensor mounted on the hoist through the Bluetooth module; the infrared thermal imaging module and the camera are used for synchronously collecting infrared thermal imaging data and visible light image data of a hydraulic hoist area. The system is used for solving the problems that the omission ratio of manual inspection is high, single infrared detection is easily interfered by the environment, fixed monitoring has a blind area, and static image analysis is poor in vibration resistance in oil leakage detection.

The invention discloses an intelligent barley pest breeding system, and relates to the technical field of barley pest breeding, and the system comprises an environment regulation and control module, an automatic feeding module, a growth monitoring module, an intelligent processing module and an early warning prompt module. A full-life-cycle data acquisition system covering an egg stage, a larva stage, a pupal stage and an adult stage is constructed, AI image analysis of a visual monitoring unit can accurately identify insect body length and body surface abnormity, a physiological monitoring unit tracks body weight change and body temperature fluctuation in real time, and a growth prediction model is combined to predict the growth of insects. According to the method, the incubation time and key growth nodes of an ecdysis window are accurately pre-judged, the strategy optimization unit dynamically adjusts environment parameters based on the growth stage, cross-dimension correlation analysis of breeding data is achieved, data support is provided for formulating a standardized breeding process, and the industry is promoted to be changed into intelligent management of data driving from experience driving.

The invention relates to the technical field of image analysis and mode recognition, in particular to a state monitoring and fault diagnosis method and system based on artificial intelligence. The invention discloses a state monitoring and fault diagnosis method based on artificial intelligence. The method comprises the following steps: S1, acquiring oil sample type data, performing background purification processing, and then executing a high-precision panoramic imaging process; the X-direction shooting number and the Y-direction shooting number are obtained based on the two-axis motion platform; s2, generating a grid coordinate sequence covering the deposition area according to the X-direction shooting number and the Y-direction shooting number, and forming a line-by-line scanning path; and synthesizing a full-focal-plane image based on the scanning path, and outputting a fused panoramic depth panorama according to the synthesized full-focal-plane image. According to the invention, through background purification, panoramic imaging, improved Mask-R-CNN mode recognition model segmentation and multivariate index fusion, precise recognition of wear particles and intelligent early warning and grading of wear states are realized.

The invention discloses a fault analysis method and system for an environment-friendly marine high-expansion foam fire extinguishing system, and relates to the field of foam fire extinguishing system.The method comprises the steps that a foam fire extinguishing agent concentrated solution is prepared, the foam fluidity of the foam fire extinguishing agent concentrated solution is analyzed, and the method is combined with a measuring device for use process simulation; meanwhile, collecting a process image generated in the simulation process; analyzing a precipitation process and a coarsening process of the foam extinguishing agent concentrated solution according to the process image, and judging the foam stability of the foam extinguishing agent concentrated solution by combining a performance testing technology; on the basis of foam fluidity and foam stability, the reliability of the foam extinguishing agent concentrated solution under the preparation proportion condition is predicted, and the fault generation probability of the high-expansion foam extinguishing system is analyzed according to the reliability. According to reliability prediction and fault probability analysis, the use state of the foam extinguishing agent concentrated solution can be further analyzed, possible fire extinguishing system faults are warned in advance, and accidents are avoided.

The invention belongs to the technical field of building monitoring, and discloses a civil engineering building monitoring system and method, and the system comprises a data collection module which collects the data, geological data and maintenance data of a civil engineering building; the environment data acquisition module acquires surrounding environment data of a building in real time; the underground water level monitoring module acquires underground water level data in real time; collecting meteorological data of the building area in real time; the building structure monitoring module collects building structure data in real time; the inspection module is used for monitoring the building in all directions; the image analysis module identifies abnormal conditions; the infrared data analysis module analyzes the infrared data; the comprehensive evaluation module comprehensively evaluates the abnormal condition; and the risk prediction module fuses the multi-source data and predicts potential problems and risks. According to the invention, abnormal conditions of the building are comprehensively evaluated; and the risk prediction module performs multi-source data fusion, comprehensively analyzes a large amount of different types of monitoring data, and predicts potential problems and risks. Preventive measures are taken in advance.

The invention discloses a small and micro water body quality monitoring method and system based on the Internet of Things, and relates to the field of water body quality monitoring, the system is composed of a plurality of functional modules, and the system comprises a data acquisition module which uses a laser radar and an RGB camera carried on an unmanned aerial vehicle to acquire a whole image of a water body, and uses Canny edge detection to extract a water body boundary; according to the boundary of the water body, the unmanned aerial vehicle flies in a gridding path, laser vertical irradiation is carried out on the water surface, and point cloud data are collected; during vertical irradiation, collecting a water surface image of a vertical area; the point cloud processing module is used for carrying out noise reduction processing on the point cloud data by utilizing statistical filtering; on the basis of IMU and real-time position data, auxiliary positioning is carried out, an NDT algorithm is adopted to carry out registration on the point cloud data, three-dimensional parameter calculation is carried out on the registered point cloud data, and vegetation points and non-vegetation points are distinguished through machine learning; and the image analysis module is used for carrying out RGB color space clustering on the water surface image.

The invention belongs to the technical field of surveying and collecting equipment, and discloses geographic information surveying and collecting equipment and method for urban and rural planning, and the method comprises the steps: a data collection module collects geological data, building data and vegetation data of an urban and rural planning region; the route planning module plans an optimal flight route of the unmanned aerial vehicle; the unmanned aerial vehicle carries a data acquisition module to carry out data survey and acquisition on urban and rural planning regions; the image analysis module analyzes the collected image; the thermal imaging analysis module analyzes the collected thermal images; the radar data analysis module performs preprocessing identification and modeling on the point cloud data acquired by the laser radar; and the model building module fuses the image data, the thermal imaging data and the radar data to build a two-dimensional or three-dimensional model of the urban and rural planning region. According to the invention, the working efficiency can be improved, data acquisition is carried out on urban and rural planning regions from different angles, image data, thermal imaging data and radar data are fused, and an accurate two-dimensional or three-dimensional model is constructed.

The invention belongs to the technical field of hydraulic engineering monitoring, and discloses a hydraulic engineering inspection system and method, and the method comprises the steps: a positioning monitoring deployment module reasonably distributes the number and positions of positioning monitoring assemblies; the route planning module plans an optimal unmanned aerial vehicle flight route; planning an advancing route, a water entry position and a water surface leaving position of the underwater robot; the cruise monitoring module carries out all-directional inspection on a water conservancy area; the image analysis module identifies abnormal conditions; the infrared data analysis module recognizes abnormal conditions in time; the radar data analysis module identifies abnormal conditions; the comprehensive analysis module comprehensively evaluates the water conservancy condition; the risk prediction module predicts potential problems and risks. According to the invention, the positioning monitoring assembly and the cruise monitoring module are combined to realize omnibearing monitoring of a water conservancy area; omnidirectional three-dimensional inspection is realized, multi-modal data collaborative analysis is realized, abnormal conditions are identified, potential problems and risks are predicted, and possible faults or disaster hidden dangers of water conservancy facilities can be found in advance.

Systems and methods for performing a medical imaging analysis task using a foundation model are provided. One or more 3D (three-dimensional) medical images each comprising a plurality of 2D (two-dimensional) slices are received. A first set of features is extracted from the plurality of 2D slices of the one or more 3D medical images using a machine learning based encoding network. Each respective feature of the first set of features is resampled based on a spatial location of one or more pixels of the 2D slices from which the respective feature was extracted. The resampled first set of features is encoded into a second set of features. A medical imaging analysis task is performed based on the second set of features. Results of the medical imaging analysis task are output.

The invention belongs to the technical field of artificial intelligence, and provides a closed space unmanned aerial vehicle autonomous obstacle avoidance inspection method and equipment based on AI image analysis, and the method comprises the steps: firstly obtaining fixed structure features and multi-source data of a closed space; then, according to the fixed structure features, the multi-source data and an improved visual SLAM algorithm, high-precision positioning and three-dimensional map construction are carried out in a GPS-free environment, and dynamic obstacles are recognized in the constructed three-dimensional map in real time; according to the IMU data, the real-time pose of the unmanned aerial vehicle is determined; and finally, according to the real-time pose, the three-dimensional map and the dynamic obstacle information, a deep reinforcement learning model is adopted to carry out path planning and obstacle avoidance decision making. According to the method, enhanced loopback detection is realized through multi-source data fusion and an improved visual SLAM algorithm in combination with fixed structure features of a closed space, positioning accumulative errors in a GPS-free environment are effectively eliminated, and global consistency and positioning precision of a three-dimensional map are remarkably improved.

The invention provides a breast cancer-oriented traditional Chinese medicine syndrome type intelligent identification method and system, a terminal and a medium, and relates to the technical field of artificial intelligence and traditional Chinese medicine tongue diagnosis image analysis. Inputting the tongue picture image into a pre-constructed tongue picture element classification model to obtain a tongue picture element classification result; acquiring pulse condition data and clinical symptom data of the breast cancer patient; and according to the tongue picture element classification result, the pulse condition data, the clinical symptom data and a plurality of preset candidate syndromes, utilizing a preset matching function to calculate a matching score corresponding to each preset candidate syndrome, and taking the preset candidate syndrome with the highest matching score as a final syndrome. According to the invention, the analytic performance of intelligent recognition of the breast cancer-oriented traditional Chinese medicine syndrome type can be improved. Wherein the tongue picture element classification model is improved on the basis of a YOLOv8n-cls network architecture, the tongue picture classification accuracy can be improved, and meanwhile, the model deployment cost is effectively reduced.

Systems and methods for performing a medical imaging analysis task conditioned on multi-domain medical images with missing modalities are provided. 1) one or more medical images each in a different domain and 2) a domain code defining a presence of the different domains in a set of predefined domains are received. One or more weights are determined based on the domain code. One or more parameters of a machine learning based encoder are updated based on the one or more weights. Features are extracted from the one or more medical images using the machine learning based encoder with the one or more updated parameters. A medical imaging analysis task is performed based on the extracted features. Results of the medical imaging analysis task are output.

The present invention provides a method for calibrating the radiation brightness of a spectral imager. The method uses a spectral imager that has completed radiation calibration in combination with a BP neural network to complete radiation calibration for a spectral imager that responds to a known DN value, thereby solving the problem of traditional methods relying on expensive calibration instruments, complex calibration operations, and harsh experimental conditions. It is only necessary to use an uncalibrated spectral imager and a calibrated spectral imager to simultaneously shoot several images of unknown scenes, and analyze and calculate the spatial geometric relationship between the shooting angles and image resolutions of the calibrated spectral imager and the uncalibrated spectral imager. The BP neural network is used to fit the mapping relationship between the DN value image of the calibrated spectral imager and its radiation brightness image. Based on the numerical relationship between the DN value images shot by the two spectral imagers and the analyzed spatial geometric relationship, the radiation brightness is calculated from the DN value shot by the uncalibrated spectral imager using the fitted mapping relationship.

A highly convenient observation device is provided. The observation apparatus includes: an observation unit configured to generate an observation image of an observation object; the analysis unit is used for analyzing the observation object; and an information processing unit for communicating with the observation unit and the analysis unit. The analysis unit includes an analysis beam device for emitting an analysis beam toward the observation object. The information processing unit includes a cluster region specifying unit, an order determining unit, and an analysis executing unit. The cluster region specifying unit specifies a plurality of cluster regions included in the observation image. The order determination unit determines an order in which the cluster regions are analyzed. The analysis execution unit causes the analysis beam device of the analysis unit to sequentially irradiate the cluster region with the analysis beam in accordance with the analysis order determined by the order determination unit.

The present application relates to the technical field of image analysis, in particular to a remote sensing image processing method and system based on AI and computer vision fusion, comprising the following steps: based on input multispectral image data, analyzing the spectral distribution data of the image, grouping and numerical operation on the multispectral channel matrix, extracting the distribution characteristic value and the explicit and implicit parameters, completing matrix decomposition through matrix calculation and classification operation, and generating spectral sparse matrix eigenvalue. In the present application, through spectral distribution analysis and channel matrix numerical operation on multispectral image data, deeper spectral and implicit features can be mined, the analysis accuracy is improved, the spectral and spatial characteristics are effectively decoupled by combining the spatial position matrix, the accuracy and efficiency of feature extraction are improved, the characteristic distribution expression ability is optimized through the screening and weighted enhancement of explicit and implicit characteristics, the target recognition accuracy is improved, the dynamic mapping of spectral and spatial characteristics accurately captures the changes of the ground surface, and the stability and accuracy of classification are enhanced.

This invention relates to the field of image analysis technology, and more specifically, to a method for extracting and analyzing the dynamic range of images of damaged landforms in mining areas. The method includes: Step 1: Integrating the multispectral radiance within a set wavelength range, while subtracting two atmospheric scattering terms at each wavelength point to obtain the corrected reflectance; Step 2: Multiplying the Euclidean distance by the curvature amplitude in the near-infrared band to obtain a spectral collapse-curvature index sensitive to browning / chlorosis; Step 3: Calculating the peak elastic strain energy density per unit area of ​​overburden-coal mass; Step 4: Extracting all linear cracks from remote sensing images of the same time period and statistically analyzing their orientation histograms, then using the Shannon entropy formula to calculate the anisotropic entropy modulation of the cracks, thereby constructing a comprehensive dynamic range damage index to characterize the degree of damage to each pixel. This invention achieves accurate identification and intensity classification of surface damage areas under mining disturbance in mining areas.

The invention discloses an ultrasonic self-adaptive scanning method and system for EIT dynamic path planning, and belongs to the technical field of medical imaging. Reconstructing an impedance distribution image in real time based on EIT and detecting an abnormal region; the coordinates of the abnormal area are mapped to an ultrasonic coordinate system, and breathing phase compensation is carried out; planning an ultrasonic beam scanning path according to clinical constraint conditions; controlling the ultrasonic beam to scan along the planned path; performing image analysis on the scanning area; and updating system parameters according to an analysis result. According to the method, abnormal coordinates detected by EIT are accurately converted into an ultrasonic beam deflection angle, clinical constraint path planning of a high-cost area of a rib shielding area and breathing phase dynamic compensation taking end expiratory as a benchmark are combined, so that the system realizes full-closed-loop control from abnormal sensing, path planning to beam execution, and the accuracy of the system is improved. The problems of scanning failure caused by rib shielding and coordinate drifting caused by respiratory movement in clinic are effectively solved, and the reliability of dynamic monitoring of the thoracic cavity is remarkably improved.

The embodiment of the invention provides a pop-up window processing method and device and a storage medium. In the embodiment of the invention, based on a collaborative architecture of two AI models, pop-up window processing is divided into two stages of potential pop-up window area analysis and pop-up window feature recognition, a potential pop-up window area in a screen image is analyzed by using a first AI model, and a sub-image of the potential pop-up window area is cut out from the screen image; and performing multi-dimensional feature analysis and popup window feature identification based on the sub-images by using the second AI model, positioning position information of closing options according to the multi-dimensional feature analysis and the popup window feature identification, executing popup window closing operation based on the position information, cutting out the sub-images of potential popup window areas, converting global analysis into local analysis, reducing calculation overhead of irrelevant areas, and on the basis, improving the popup window closing efficiency. Model reasoning is used for analyzing the cut sub-images, the efficiency and accuracy of pop-up window detection and closing are effectively improved, then the probability that automatic testing is interrupted due to pop-up window interference is reduced, and the testing stability and continuity are improved.

The present invention discloses a method, device and computing equipment for pushing image reports based on RPA and AI. The method includes: using a preset lung segmentation model to segment the lung medical image to be detected sent by the RPA robot to obtain a target medical image containing only the lung area, using a preset lung nodule detection model to detect the target medical image to obtain attribute information of each suspected lung nodule, using a preset lung nodule segmentation model to segment the area where each suspected lung nodule is located to obtain three-dimensional contour information of each suspected lung nodule; generating a suspected lung nodule image report based on the attribute information and three-dimensional contour information of each suspected lung nodule and sending it to the hospital platform through the RPA robot. In this way, the lung nodules are detected by AI image analysis technology to obtain a suspected lung nodule image report, and the report is sent to the hospital platform through the RPA robot, which reduces the time doctors spend identifying lung nodules and improves efficiency.

Systems and methods for performing a medical imaging analysis task are provided. 1) One or more input medical images each in a domain and 2) a domain code for each of the one or more input medical images identifying its domain are received. Each particular input medical image of the one or more input medical images is downsampled using a dynamic convolutional layer based on the domain code for the particular input medical image. A first set of features is extracted from each particular downsampled input medical image of the one or more downsampled input medical images using the dynamic convolutional layer based on the domain code for the particular downsampled input medical image. The first set of features is encoded into a second set of features using a machine learning based encoder. A medical imaging analysis task is performed based on the second set of features using a machine learning based decoder. Results of the medical imaging analysis task are output.

The application discloses a radar chart-based ground foreign matter detection method, device, equipment and medium. The method comprises the following steps: acquiring a current radar image of a radar detection area; obtaining a current contour frame information library according to the current radar image; wherein the current contour frame information library comprises current contour frame information of at least one to-be-detected target; and performing foreign matter detection on the to-be-detected target according to the current contour frame information and a historical contour frame information library of the to-be-detected target; wherein the historical contour frame information library is determined according to the historical radar image. According to the technical scheme, accurate current contour frame information library can be obtained through analysis and processing of the current radar image, and then the current contour frame information of the to-be-detected target is compared with the historical contour frame information library, so that whether the to-be-detected target is a foreign matter can be accurately and quickly detected.

The present application provides a power tower mechanical state detection device, method, medium and program product. The power tower mechanical state detection device includes a control device, a collection device and a flight device. The control device is configured to: control the flight device to move to the corresponding position of the target power tower, and control the collection device to collect hyperspectral images of multiple steel structures at different positions; analyze the corrosion condition of the steel structures based on the hyperspectral images of the steel structures, and use a pre-built post-corrosion mechanical property model to predict the residual bearing capacity of the steel structures; perform mechanical property simulation based on the residual bearing capacity of each steel structure to predict the health status of the target power tower; when it is predicted that the target power tower has a safety risk, perform a safety risk classification warning on the target power tower, greatly reduce the detection time, and reduce problems such as missed detection and misjudgment, thereby improving the accuracy and efficiency of power tower state detection.

Embodiments of the present disclosure provide systems and methods for analyzing medical images containing vascular structures using a sequential model. An example system includes a communication interface configured to receive the medical image and the sequential model. The sequential model includes a vessel extraction sub-model and a lesion analysis sub-model. The vessel extraction sub-model and the lesion analysis sub-model are trained independently or jointly. The example system further includes at least one processor configured to apply the vessel extraction sub-model to the received medical image to extract location information of the vascular structure. The at least one processor also applies the lesion analysis sub-model to the received medical image and the location information extracted by the vessel extraction sub-model to obtain a lesion analysis result of the vascular structure. The at least one processor further outputs the lesion analysis result of the vascular structure.

The invention relates to the technical field of EL detection, in particular to an EL test device and method used in the daytime environment, and the device comprises a test board which is used for placing an EL test computer to operate an EL test system; the environment simulation frame is arranged on one side of the test board; and the shading assembly is mounted at the inner top of the environment simulation frame. Through accurate environment simulation and shading processing and flexible camera and light source adjustment, the EL test can be ensured to obtain the same effect as that of night test in a daytime environment, so that the test accuracy is improved, test parameters can be automatically adjusted, images can be automatically shot, defects can be automatically analyzed, a test report can be automatically generated, manual intervention is greatly reduced, and the test efficiency is improved. Therefore, the device and the method provided by the invention are suitable for testing the batteries with different sizes and types, and provide powerful support for quality control in the research, development, production and use processes of the batteries.

A navigation system for a host vehicle is provided. The system may comprise at least one processing device programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify at least one vehicle-induced occlusion zone in an environment of the host vehicle; and cause a navigational change for the host vehicle based, at least in part, on a size of a target vehicle that induces the identified occlusion zone.

The invention relates to the field of image analysis, and discloses a placenta implantable disease grading diagnosis method based on a multi-modal deep fusion model. The method comprises the following steps: collecting patient information including basic information, ultrasonic images, imaging examination results and pathological examination results, and constructing a data set for training a placenta implantable disease grading diagnosis model according to the patient information; constructing and training a numerical feature encoder and an ultrasonic image encoder by using the data set; and constructing and training a multi-modal deep fusion model by using the data set, the trained numerical feature encoder and the trained ultrasonic image encoder. According to the method, the problem of insufficient accuracy of the single-mode model in the classification diagnosis task of the placenta implantable disease is solved, and the diagnosis performance is effectively improved.