248 results about "Imagery analysis" patented technology

The invention belongs to the technical field of road deformation monitoring, and discloses a road slope deformation monitoring system and method, and the system comprises a fixed point monitoring mechanism which monitors the displacement, strain, underground water level and inclination parameters of a slope in real time at key points and key positions of the road slope; the route planning module plans the flight route of the unmanned aerial vehicle; the cruise monitoring mechanism is used for monitoring the side slope in all directions; the weather data acquisition module is used for acquiring local weather data; the three-dimensional laser scanner data analysis module is used for identifying abnormal conditions on the side slope; the image analysis module is used for identifying abnormal conditions on the slope; the comprehensive evaluation module is used for comprehensively evaluating the stability of the slope by utilizing a comprehensive evaluation model; and the alarm module comprises an alarm. According to the invention, the fixed-point monitoring mechanism and the cruise monitoring mechanism are combined to monitor the slope in all directions, the comprehensive evaluation module fuses data of various monitoring means, and the comprehensive evaluation model is utilized to comprehensively evaluate the slope stability.

The invention relates to the technical field of garden monitoring, in particular to a landscaping monitoring system and method, and the system comprises a collection module which is used for obtaining multi-angle images of a garden in stages, and selectively collecting the images according to a correlation judgment result; the analysis module is used for extracting vegetation indexes, disease targets, leaf health features and falling object state features; the identification selection module is used for generating a dynamic acquisition strategy according to the relevance abnormal index and triggering the acquisition module to directionally acquire the second-level image; the weighted fusion module is used for performing adaptive weighted fusion on the multi-source features and calculating a comprehensive wilt risk level; the early-warning module is used for generating early-warning signals and early-warning grades, the view limitation of traditional single-view-angle monitoring is broken through, large-range vegetation vitality abnormal areas can be rapidly screened, detail features of trunk diseases, low leaf withering and microscopic disease spots can be accurately captured, and the comprehensiveness and reliability of early withering recognition are remarkably improved.

The invention relates to a road state monitoring system based on video image analysis, which belongs to the technical field of road monitoring and comprises the following steps of: measuring the speed of a target truck vehicle on a key road section of an express lane; when the vehicle speed is lower than a preset speed threshold value, capturing a scene to obtain a captured video image; analyzing the traffic condition of the scene through the snapshot video image; and judging whether the target truck has traffic violation according to the traffic condition. According to the invention, the problem that traffic violation may be misjudged due to the fact that the influence caused by lane occupation cannot be judged according to actual traffic conditions in the prior art is solved. And identifying vehicle distribution information behind the target truck vehicle in the expressway, and predicting the probability of traffic congestion at present by adopting a Bayesian network model according to the vehicle distribution information. According to the method, the accuracy of traffic condition analysis is improved by using the Bayesian network model, and reference is provided for traffic decision making.

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 discloses an inspection robot infrared vision AI analysis system, and relates to the technical field of AI inspection. The inspection robot infrared vision AI analysis system comprises an infrared imaging module used for capturing temperature data of a target device and a surrounding environment thereof in real time and generating an infrared image; the AI analysis module is connected with the infrared imaging module, and is used for processing the image data by adopting algorithms such as a convolutional neural network (CNN) and identifying temperature abnormity or equipment faults; the sensor fusion module comprises an infrared sensor, a laser radar and an ultrasonic sensor; the automatic navigation module can automatically plan an inspection path according to the real-time environment information and the inspection task, and has an obstacle avoidance function; the cloud platform data management module is used for receiving the inspection data and generating an equipment health report; the infrared vision technology, AI analysis and fusion of various sensors are combined, so that more accurate fault prediction, equipment monitoring, automatic inspection and partial self-repairing functions can be realized.

The invention provides a control system, method and equipment based on intelligent treatment of flotation gas. The system comprises a monitoring unit, an evaluation unit, an operation unit, an identification unit and a control unit, the evaluation unit is connected with the monitoring unit and the operation unit. The control unit is connected with the operation unit and the recognition unit. The monitoring unit is used for monitoring flotation gas information of the target area; the evaluation unit is used for evaluating the flotation gas information by adopting a preset artificial intelligence (AI) algorithm to obtain an evaluation result corresponding to the flotation gas information; the operation unit is used for determining operation information related to the exhaust fan in the target area based on the evaluation result; the recognition unit is used for collecting picture information corresponding to abnormal working conditions in the target area and determining a recognition result corresponding to the picture information by adopting a preset AI image analysis algorithm; and the control unit is used for controlling technological parameters corresponding to the flotation gas in the target area based on the operation information and the recognition result.

The invention relates to the technical field of smart home, in particular to a sweeping robot movement obstacle avoidance method and system. The sweeping robot movement obstacle avoidance method is applied to the sweeping robot, the sweeping robot comprises a radar scanning device, a camera device and a control chip, the method is applied to the control chip, and the method comprises the steps of obtaining a scanning result of the radar scanning device and a field image shot by the camera device; the scanning result and the field image are analyzed, and the obstacle type of the to-be-cleaned area is determined; and according to the obstacle type, determining whether an uncleanable obstacle exists or not. According to the type and characteristics of the obstacle, the sweeping feasibility of the sweeping robot on the type of obstacle is evaluated, and unnecessary cleaning attempts and damage to the obstacle are avoided. The obstacle avoidance path is planned in advance by predicting the motion trail of the dynamic obstacle, and it is ensured that the sweeping robot can safely avoid the dynamic obstacle.

The invention relates to the technical field of water quality monitoring, in particular to an artificial intelligence river water quality pollutant spatial form monitoring system and method, and the system comprises a concentration trend analysis module, a spatial form monitoring module, a spatial trend analysis module and a pollution early warning evaluation module. According to the method, the water quality is monitored at a plurality of river water area nodes, the pollutant concentration data can be accurately captured, the change rate of the pollutant concentration can be predicted, the understanding of the pollution trend is improved, the response speed to the pollution event is enhanced, the image monitoring data is combined, the convolutional neural network is used for analyzing the river water area image, and the accuracy of the analysis is improved. The spatial area of the pollutants is analyzed, so that the spatial distribution state of the pollutants is evaluated in detail, the severity level of the pollutants can be evaluated, managers are notified in real time, meanwhile, dynamic change information of pollutant concentration and spatial distribution is integrated, an early warning mechanism is more agile and accurate, and the early warning efficiency is improved. And the effectiveness and the accuracy of pollution early warning are improved.

The invention belongs to the field of intelligent management, and relates to a building automation construction site intelligent management system based on big data, and the system comprises a building construction size qualification analysis module, a reinforcing steel bar quality qualification judgment module, a building construction quality qualification judgment module, a building construction model splicing module, a fabricated residence qualification judgment module and the like. The method comprises the steps that firstly, building component size data are collected, an actual model is created in BIM software, and the area qualification degree is analyzed; then, steel bar distribution and defects are analyzed based on the model, and steel bar quality is judged. And then, the building component quality is judged and early warning is performed by integrating the area and the steel bar quality. And if all the components are qualified, simulating splicing to obtain an integral model, and judging the qualification of the integral model. And finally, carrying out field assembly and concrete filling, collecting images, analyzing wall pollution and defects, comprehensively evaluating the quality of the fabricated residence and carrying out early warning. The system ensures comprehensive quality detection and qualification judgment of the assembly type residence from components to the whole.

A system and method for patient diagnosis and implementation of orthodontic apparatuses and treatments. The patient can be located remotely from a remote treatment system, thereby allowing for automatic diagnosis and treatment of the patient without interaction with a medical professional or associated staff. A neural network of an artificial intelligence engine (AI engine) of the remote treatment system can utilize images and other data captured by a patient, as well as patient responses to questionnaires, to derive information regarding the patient's symptoms, and the severity of those symptoms. The AI engine can further be adapted, including via use of historical information regarding a plurality of patients and information from the patient, to obtain a diagnosis and treatment recommendation. The AI engine can further be configured to determine, based on analysis of patient provided images, an appropriately sized orthopedic apparatus, which can be sent to the patient for implementation.

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 discloses a suspended rail type mobile platform immersed tunnel monitoring system based on binocular vision, which is characterized in that a mobile platform is suspended on a rail at the top of an immersed tunnel in a walking manner, and a binocular vision module, a water stain crack identification module, an environment monitoring module and other monitoring modules are carried on the mobile platform. Therefore, the internal environment of the immersed tunnel can be inspected. The invention further designs a two-dimensional code positioning auxiliary mark system and a binocular measurement auxiliary mark system which are used in cooperation with the mobile platform, and the two-dimensional code positioning auxiliary mark system is used for providing accurate position information and mark number information of joints of all pipe joints for the mobile platform. The binocular measurement auxiliary mark system comprises marker groups installed at the joints of all the pipe joints of the immersed tunnel, when the mobile platform works, the marker groups are photographed through a binocular vision module on the mobile platform, and then whether the joints of the pipe joints of the immersed tunnel deform or not is analyzed according to obtained images.

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 invention discloses an unmanned aerial vehicle forest inspection method and system based on Beidou positioning, and relates to the field of forest inspection, and the system comprises an area limiting module which is used for uploading position coordinates and limiting an inspection area based on the uploaded position coordinates; the control module is used for controlling the unmanned aerial vehicle to fly in the inspection area and collecting forest images; the analysis module is used for receiving the forest image collected in the flight process of the unmanned aerial vehicle, storing the forest image and analyzing the forest health situation tendency based on the stored forest image; according to the invention, by defining the inspection area and designing and deploying the analysis logic of the unmanned aerial vehicle, the unmanned aerial vehicle can autonomously and intelligently complete forest image acquisition, and in the forest image acquisition process, the forest image acquisition path is autonomously and randomly generated, so that the diversity of forest image acquisition is ensured; and the result of the system for subsequently evaluating whether the forest ecological environment is healthy is more reliable and effective.

The invention discloses an urban garbage removal and output prediction method and system based on deep learning, and the method comprises the following steps: S1, obtaining a road video image, analyzing the garbage distribution and density, predicting the garbage distribution evolution, and estimating the garbage amount of a cleaning region; s2, according to the estimated garbage amount and sweeping area distribution, a sanitation vehicle is dispatched for sweeping, a video image in sweeping is collected, and the rotating speed and the vehicle speed of a sweeping disc of the vehicle are adjusted; and S3, the difference between the actual garbage amount and the estimated garbage amount is analyzed and used for correcting the estimated garbage amount. Intelligent management of environmental sanitation work is achieved, garbage distribution and cleaning amount can be efficiently estimated, a scheduling scheme and cleaning parameters can be dynamically adjusted according to actual conditions, the cleaning efficiency and quality are greatly improved, meanwhile, through data analysis and model optimization, the accuracy and reliability of the system are enhanced, and the cleaning efficiency and quality are improved. The improvement of the urban environmental sanitation level and the reasonable utilization of resources are effectively promoted.

The invention relates to the field of synthetic aperture radar imaging, in particular to an SAR image analysis method based on synthetic aperture radar multi-source data fusion, and the method comprises the steps: firstly obtaining the transmitting signal intensity and echo signal intensity of each polarization channel of a synthetic aperture radar at different moments; obtaining a state characteristic value of each polarization channel at each moment according to the difference between the transmitting signal strength and the echo signal strength of each moment in each polarization channel and the echo signal strength of each moment in all polarization channels; analyzing the correlation between the polarization channels to obtain a correlation coefficient, and fusing the echo signal intensities of all the polarization channels at the same moment through the obtained fusion adjustment parameters to obtain fusion signal data at each moment; and generating an SAR image based on the fused signal data at all moments. According to the invention, the fusion effect of the echo signal data in the multi-polarization channel can be improved, and a radar image with better quality can be generated.

The invention provides a detection threshold adaptive change detection method, and relates to the technical field of remote sensing image analysis, and the method comprises the steps: carrying out the super-cooperative super-pixel segmentation of a remote sensing image before and after the change, and obtaining the super-pixel segmentation results of the remote sensing image before and after the change; calculating an affinity matrix of the remote sensing image before and after the change; selecting a super-pixel-level training sample according to the super-pixel segmentation result of the remote sensing image before and after the change and the affinity matrix; training a Copula function through the super-pixel-level training sample, and generating an adaptive change detection threshold value by using the trained Copula function; and calculating the test statistics of the remote sensing image before and after the change through the trained Copula function, and carrying out detection judgment on the test statistics by using an adaptive change detection threshold to obtain a change detection result. According to the scheme, the method is suitable for change detection of remote sensing images in various remote sensing scenes.

The invention relates to the technical field of image analysis and processing, in particular to a typhoon positioning and intensity determining method and device based on remote sensing data, and the method comprises the following steps: carrying out the radial gradient enhancement processing of a microwave brightness temperature remote sensing image, and extracting a gradient extreme point distribution diagram of an eye wall region; performing morphological segmentation on the infrared cloud top temperature remote sensing image to generate a binary contour feature map representing an eye region structure; and inputting the gradient extreme point distribution map and the binary contour feature map into an improved convolutional fusion network, and outputting typhoon center coordinates and strength grade parameters. According to the method, the continuous boundary is reconstructed under the complex condition that a double-eye wall or a broken eye wall exists, the adaptive capacity of a positioning algorithm to an asymmetric and fragmented structure is improved, the calculation range is limited through an eye region constraint mechanism, the interference data participation degree is reduced, and higher energy efficiency ratio and engineering deployability are achieved.

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 a stacked goods tiling method and system. The method comprises the steps that whether a goods stacking area exists in each climbing section or not is analyzed and determined according to images, collected by an image shooting device, of each climbing section of a stacked goods tiling machine; each climbing section is provided with a plurality of parallel and independent conveying subareas; and when it is determined that the cargo stacking area exists in any climbing section, at least adjusting the conveying speed of the cargo overlapping area of each cargo stacking area in the conveying subarea corresponding to the climbing section so as to eliminate the cargo stacking area. The cargo stacking area can be eliminated only by performing acceleration and deceleration control on the local conveying subarea corresponding to the cargo stacking area, the acceleration and deceleration control of unnecessary conveying subareas can be reduced, other cargoes without the cargo stacking area can be normally conveyed to improve the efficiency, and meanwhile, the cargo stacking area can be reduced. According to the method, the cargo stacking area can be eliminated through acceleration and deceleration in the whole process of automatically recognizing the cargo stacking area and moving the cargo stacking area out of the stacked cargo tiling machine.

The invention relates to the technical field of traffic management, in particular to a road target detection system, method and device, and the method comprises the steps: collecting a vehicle image of a roadside parking region; analyzing the vehicle image data to detect vehicle information in the parking space; based on the vehicle information, blind area vehicles generated due to shielding of front and rear vehicles are recognized and marked; recording parking information of a vehicle in a parking space in a blind area, wherein the parking information comprises mark information, a position and parking time; after the vehicle in the blind area is driven out of the parking space, whether the marked vehicle is still in a shielding state or not is checked; if the marked vehicle is still in the shielding state, continuously acquiring feature information of the marked vehicle; searching and matching corresponding vehicles in other cameras in a preset range based on the feature information to obtain a target vehicle; and obtaining license plate information based on the target vehicle through a license plate recognition technology, and replacing the mark information in the parking information with the license plate information.

The invention relates to a computing device or a method of generating a spatial and temporal map (50) of an estimated vessels traffic in an area (51), said method comprising the following steps: Acquisition (110) of a plurality of satellite radar images of the area (51), said plurality of satellite radar images having been generated over a period of at least twelve months at a median frequency lower than ten images per week; Analysis (120) of the plurality of satellite radar images to generate data on vessels forming an historical vessel traffic; Correction (130) of the generated data based on the plurality of satellite radar images frequency; and Generation (180), from the corrected data, of at least one spatial and temporal distribution map of vessels forming the estimated vessel traffic in the area, said spatial and temporal distribution map comprising vessel density values by time period.

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 discloses a complete polarization synthetic aperture radar and visible light image text description method. The method comprises the following steps: acquiring a complete polarization synthetic aperture radar image and a visible light image; carrying out Lee filtering denoising processing on the image, fusing the features of a plurality of polarization channels, inputting the fused features into a visual converter encoder for feature extraction, and carrying out multi-layer converter feature extraction on the visible light image; and performing weighted fusion on the multilayer features of the visible light image and the synthetic aperture radar image, and inputting the fused multi-modal features into a text to a text migration converter model text encoder to generate natural language description. According to the method, the complementary advantages of the fully polarized synthetic aperture radar and the visible light image are fully exerted through the deep learning technology, dynamic fusion and semantic interaction of multi-modal features are achieved, the accuracy and robustness of automatic text description of the remote sensing image are remarkably improved, and the method is suitable for multi-modal remote sensing image analysis and understanding in a complex environment.

The invention provides a medical image quality control and organ phenotype extraction system, relates to the technical field of image analysis, and constructs a topological relation tree of organ phenotypes according to relevance of anatomical structures among organ phenotype features in a medical image. Determining a structure association graph of the organ phenotype in the medical image based on the topological relation tree and the distribution information of the organ contour in the organ phenotype feature; determining a semantic structure chart of the medical entities in the text report through a context relationship of clinical description statements in the text report and a logic association relationship between the medical entities; performing node-level alignment fusion on the structure association graph of the organ phenotype and the semantic structure graph of the medical entity to obtain an organ-entity feature association graph; and performing quality control judgment on the medical image data based on the feature correlation map, and if the medical image data is judged to have imaging quality abnormality, outputting a quality control warning. Based on the scheme, the multi-modal correlation quality control of the medical image data can be realized, so that the reliability of the data is improved.

The invention relates to a safety production supervision system and method based on artificial intelligence and big data analysis, and the system comprises a production equipment management system, a data and business overall planning server, a video analysis server and monitoring equipment. The video analysis server is used for forwarding and coordinating the service functions of the service terminals, docking with a fire fighting system, analyzing a security image corresponding to a field scene, and carrying out alarm reporting on an appearing scene picture conforming to security alarm; various monitoring problems in civil explosive production process application scenes are sensed, monitored, alarmed, evaluated and handled by using an artificial intelligence technology, and 24-hour uninterrupted unattended safety management is realized.

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 relates to the technical field of image analysis, in particular to a needle point position identification method for thyroid fine needle puncture ultrasound images, which comprises the following steps: acquiring each frame of target ultrasound image in the current puncture process, and determining a local difference feature value corresponding to each pixel point; determining a characteristic state change index corresponding to each pixel point; screening out a target change area from each frame of target ultrasonic image, and carrying out edge detection on the target change area; screening out a target fine needle area from each frame of target ultrasonic image; determining a fine needle motion state corresponding to each frame of target ultrasonic image according to a motion change condition between the target fine needle areas in the adjacent frames of target ultrasonic images; and based on the fine needle motion state corresponding to each frame of target ultrasonic image, identifying the needle point position in each frame of target ultrasonic image. According to the method, the target ultrasonic image is analyzed, so that the needle point position is recognized, and the accuracy of needle point position recognition is improved.

A system and method for patient diagnosis and implementation of orthodontic apparatuses and treatments. The patient can be located remotely from a remote treatment system, thereby allowing for automatic diagnosis and treatment of the patient without interaction with a medical professional or associated staff. A neural network of an artificial intelligence engine (Al engine) of the remote treatment system can utilize images and other data captured by a patient, as well as patient responses to questionnaires, to derive information regarding the patient's symptoms, and the severity of those symptoms. The Al engine can further be adapted, including via use of historical information regarding a plurality of patients and information from the patient, to obtain a diagnosis and treatment recommendation. The Al engine can further be configured to determine, based on analysis of patient provided images, an appropriately sized orthopedic apparatus, which can be sent to the patient for implementation.