A system and method for generating an interactive user interface for inspection visualization. The system includes multiple imaging devices positioned along a inspection passage and at least one processor that executes instructions to: obtain multiple sets of images of vehicle surface segments captured during relative movement between the vehicle and imaging devices; stitch the images into a dataset record mapping vehicle parts and surface anomalies; transform the image data into a moving visual media object using a first generative AI model; compute a mapping record between segmented vehicle parts and target frame areas; and transform the mapping record and visual media object into an interactive interface using a second generative AI model. The interface displays user-selectable markers synchronized with media playback, indicating anomaly locations from multiple viewing angles, and performs data retrieval and display actions based on user selection of anomalies.
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
2.
Interior defect detection through vehicle glazing using multi-view motion analysis and asymmetric illumination
A method and system for detecting interior defects in vehicles through glazing without requiring door opening or interior sensor installation. The system captures multi-view images of vehicle window panes from spatially separated viewpoints during relative motion between vehicle and imaging device. Motion field data representing pixel displacement is computed and used to classify pixels into interior-origin pixels exhibiting apparent depth behind the window pane plane and reflection-origin pixels exhibiting apparent depth at the glass surface. An interior-enhanced image is generated through motion refocusing that sharpens interior textures while blurring glass-plane reflections. Optional asymmetric illumination and polarization provide additional reflection suppression. Transmittance compensation accounts for glazing tint and spectral characteristics. Defect detection algorithms identify interior defects including seat damage, missing components, and structural defects, with privacy-aware redaction of occupants integrated into the processing pipeline.
G06V 10/60 - Extraction of image or video features relating to illumination properties, e.g. using a reflectance or lighting model
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 20/59 - Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestriansBody parts, e.g. hands
G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
3.
Vehicle inspection system and method with adaptive template matching and temporal defect tracking
A vehicle inspection system captures multi-view images and retrieves vehicle-specific reference templates that evolve through gated learning based on validated inspection outcomes. The system computes multiple spatially-registered similarity maps, generates deviation maps, and compares against prior inspections to distinguish persistent defects from transient conditions. The adaptive template approach reduces false positives while maintaining sensitivity to genuine defects, providing accurate temporal defect tracking for fleet management applications.
There is provided a method of processing data using a processor coupled to a memory, the method comprising: display via a display device of a client computing device, an interactive graphical user interface (GUI) for monitoring and managing an event-sourced system, presenting within the GUI: messages transmitted within the event-sourced system, events of the event-sourced system, and commands executed by the event-sourced system, receiving, via the GUI, a selection for emulation of execution of a message and/or a, in response to the selection for emulation, computing an emulation of execution of the selected message and/or command, within the event-sourced system, and dynamically updating messages, the events, and/or the commands, presented within the GUI, with an outcome of the emulation of execution of the selected message and/or command, within the event-sourced system.
A computer-implemented method and a system for temporal damage consistency validation, comprising receiving, at one or more processors, a current vehicle scan containing image data and damage detections for a vehicle, retrieving historical damage data for the vehicle from previous scans;, projecting geometric positions of the previously detected damage regions onto corresponding locations in the current vehicle scan using a geometric transformation that accounts for vehicle positioning differences between inspection sessions, matching the projected damage positions with current scan detections using feature vector similarity scoring by computing similarity measures between deep learning feature vectors of projected damage regions and deep learning feature vectors of damage detections found in corresponding areas of the current vehicle scan and classifying damage states based on results of the matching operation.
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
6.
Multi-modal automotive paint authentication and repainting detection system
There is provided a method of automatically detecting paint anomalies on a vehicle, comprising: operating illumination elements configured for generating illuminations of different types at first different frequency bands and/or at first different polarization angles, positioned for illuminating the vehicle, operating image sensors for capturing the images, wherein the image sensors are of different modalities configured for sensing at second different frequency bands and/or at second different polarization angles configured for capturing images of a vehicle, extracting features from the images, generating a multi-dimensional dataset by aggregating the features, feeding the multi-dimensional dataset into a machine learning model, obtaining from the machine learning model, an indication of a region of the vehicle with a paint anomaly, generating an enhanced image depicting the region of the vehicle with the paint anomaly overlaid with a visual indication of the region with the paint anomaly, and presenting the enhanced image on a display.
G01N 21/3563 - Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solidsPreparation of samples therefor
H04N 23/11 - Cameras or camera modules comprising electronic image sensorsControl thereof for generating image signals from different wavelengths for generating image signals from visible and infrared light wavelengths
H04N 23/13 - Cameras or camera modules comprising electronic image sensorsControl thereof for generating image signals from different wavelengths with multiple sensors
System and method for a computer vision technique for contactless measurement of vehicle toe are disclosed herein. The method includes capturing with a stereo camera an image containing a vehicle's wheel, segmenting the image, determining a suitable circular component of the wheel and computing the angle of the wheel relative to the camera and, subsequently, to the body of the vehicle. The method includes repeating the process for a plurality of wheels and computing the toe angle of the vehicle based on the obtained results.
G01B 11/275 - Measuring arrangements characterised by the use of optical techniques for measuring angles or tapersMeasuring arrangements characterised by the use of optical techniques for testing the alignment of axes for testing wheel alignment
8.
TIRE-BASED SCALE CALIBRATION WITH GEOMETRIC COMPENSATION AND REAL-TIME ADAPTIVE PROCESSING
A computerized system for calibrating vehicle part measurements utilizes tire-based scale calibration with image processing and geometric compensation capabilities. The system automatically determines estimated tire size data from tire images using consensus-based algorithms, extracts actual tire specifications through optical character recognition, and calculates scale calibration data for converting pixel measurements to physical units. Geometric distortion detection and compensation handle angular misalignments between camera and tire planes. Real-time continuous calibration adapts to changing conditions during vehicle inspection operations. The calibration data integrates with defect detection and severity classification systems to enable accurate physical measurements of vehicle components and defects, providing calibration accuracy within 2% across varying operating conditions while accommodating diverse tire types and operational scenarios.
There is provided a system for generating instructions for a generative model to present vehicle damage assessment. The system includes image sensors positioned at different heights and angles to capture vehicle images, a communication element for receiving images and accessing a stored model, and a processor connected to endpoint devices via a network. The processor generates instructional inputs combining damage severity classifications with vehicle images, sends these to the generative model, and obtains natural language elements for structural damage reconstitution. The system creates interactive presentations with populated template fields and classification selection elements displayed on endpoint devices. When users select different damage severity classifications in real-time, the system automatically generates new instructional inputs, obtains updated natural language elements from the generative model, and dynamically updates the presentation by replacing template fields. This enables real-time comparison of different damage severity assessments for the same vehicle damage using the same source images.
G06Q 10/20 - Administration of product repair or maintenance
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
G07C 5/12 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time in graphical form
A system for inline measurement of gap width and flush depth of adjacent surfaces of a vehicle continuously moving longitudinally relative to the system comprises at least one laser source, at least one image sensor, and a processor configured to perform measurements within a predetermined cycle time. The processor controls the laser profiler to project laser beams onto vehicle surfaces, acquires optical images of reflected beams from the image sensor, and matches gap profiles from stored one-dimensional profiles to identify gaps. The system performs gap width and flush depth measurements and outputs anomaly detection signals when measurements deviate from predetermined target values. The system includes bias correction capabilities using empirically determined values and CAD-based target values for specific vehicle models. Dynamic adjustments are made based on vehicle color, with laser brightness optimized for surface reflectivity.
G01B 11/14 - Measuring arrangements characterised by the use of optical techniques for measuring distance or clearance between spaced objects or spaced apertures
G01B 11/04 - Measuring arrangements characterised by the use of optical techniques for measuring length, width, or thickness specially adapted for measuring length or width of objects while moving
A computer-implemented method and system for validating vehicle damage detection utilizes symmetry-based analysis of opposing vehicle sides. The method comprises receiving images from opposing sides of a vehicle, detecting a damage region in one image, and computing deep learning feature vectors for the damage region and a corresponding region in the opposite image. These feature vectors represent learned visual patterns that discriminate between damage and normal vehicle features. A similarity measure is computed between the feature vectors, and the detected damage is validated based on this measure. The system includes sensors for image capture, processors, and memory storing instructions to execute the method. This approach leverages vehicle symmetry to reduce false positives, compensate for environmental variations, and improve damage detection accuracy. The method can adapt to asymmetric vehicle positioning and varying environmental conditions, providing robust performance in real-world scenarios.
There is provided a method of automatically detecting that a target image is deepfake, comprising: receiving authentic images depicting a vehicle with actual damage, receiving the target image depicting potential damage to the vehicle, feeding the target image into a machine learning (ML) model, obtaining a candidate set of human-readable text describing the potential damage to the vehicle, feeding the authentic images into the ML model, obtaining from the ML model, a ground truth set of human-readable text describing the actual damage to the vehicle depicted in the authentic images, computing a similarity metric indicating a difference between the potential damage described in the candidate set of human-readable text and the actual damage described in the ground truth set of human-readable text, and in response to the difference being above a threshold or meeting a requirement indicating a significant difference, detecting that the target image is likely deepfake.
A method and system for estimating dimensions of vehicle exterior defects using multiple cameras arranged in a predefined configuration. The method comprises receiving images from multiple strategically positioned image sensors including side cameras parallel to a vehicle height axis, diagonal cameras at an inclined angle, and roof top cameras perpendicular to the height axis. An angle to detected defects is computed based on image sensor parameters. Different distance calculations are applied based on vehicle section location, with specialized formulas for windshield, back window, and roof components. Defect sizes are computed by determining multiple defect dimensions, with at least one dimension being adjusted by an angular correction factor derived from the relationship between camera angle and vehicle surface orientation. The system implements comprehensive validation through cross-referencing between cameras, comparison with known specifications, and measurement consistency analysis across multiple frames.
There is provided a method of image processing for detection of damage on a vehicle, comprising: accessing time-spaced image sequences depicting a region of a vehicle, captured by image sensors positioned at different heights and/or angles relative to the vehicle, identifying candidate regions of damage in the time-spaced image sequences, performing multi-level redundancy validation by: executing spatial correlation between images captured by different images sensors at different heights and/or different angles, executing temporal correlation between consecutive images captured by each image sensor, and validating persistence of each candidate region of damage across a threshold number of consecutive frames, identifying redundancy in the candidate regions of damage corresponding to a common physical location of the vehicle denoting a single physical damage region based on the multi-level redundancy validation, and providing an indication of the common physical location of the vehicle corresponding to the single physical damage region.
A system and method for generating an interactive user interface for inspection visualization. The system includes multiple imaging devices positioned along a inspection passage and at least one processor that executes instructions to: obtain multiple sets of images of vehicle surface segments captured during relative movement between the vehicle and imaging devices; stitch the images into a dataset record mapping vehicle parts and surface anomalies; transform the image data into a moving visual media object using a first generative AI model; compute a mapping record between segmented vehicle parts and target frame areas; and transform the mapping record and visual media object into an interactive interface using a second generative AI model. The interface displays user-selectable markers synchronized with media playback, indicating anomaly locations from multiple viewing angles, and performs data retrieval and display actions based on user selection of anomalies.
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
16.
Detection and estimation of defects in vehicle's exterior
A method of estimating dimensions of vehicle's exterior defects based on reference dimensions comprising analyzing one or more images captured by one or more image sensors deployed to depict a vehicle to identify one or more reference feature relating to the vehicle, obtaining real-world size of the reference feature(s), computing a pixel to real-world size ratio for the image sensor(s) based on the real-world size of the reference feature(s) and a size in pixels of the reference feature(s) in the image(s), analyzing one or more images captured by the image sensor(s) to identify one or more defects in an exterior of the vehicle, computing a size in pixels of one or more dimensions of each defect; and computing a real-world size of the dimension(s) based on the size in pixels of the respective dimension and the pixel to real-world size ratio.
G06T 7/30 - Determination of transform parameters for the alignment of images, i.e. image registration
G06T 7/62 - Analysis of geometric attributes of area, perimeter, diameter or volume
G06T 15/00 - 3D [Three Dimensional] image rendering
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
17.
Detection and estimation of defects in vehicle's exterior
A method of estimating dimensions of vehicle's exterior defects based on reference dimensions comprising analyzing one or more images captured by one or more image sensors deployed to depict a vehicle to identify one or more reference feature relating to the vehicle, obtaining real-world size of the reference feature(s), computing a pixel to real-world size ratio for the image sensor(s) based on the real-world size of the reference feature(s) and a size in pixels of the reference feature(s) in the image(s), analyzing one or more images captured by the image sensor(s) to identify one or more defects in an exterior of the vehicle, computing a size in pixels of one or more dimensions of each defect; and computing a real-world size of the dimension(s) based on the size in pixels of the respective dimension and the pixel to real-world size ratio.
G06T 7/30 - Determination of transform parameters for the alignment of images, i.e. image registration
G06T 7/62 - Analysis of geometric attributes of area, perimeter, diameter or volume
G06T 15/00 - 3D [Three Dimensional] image rendering
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
18.
Systems and methods for automated inspection of vehicles for body damage
There is provided a computer implemented method of image processing for detection of damage on a vehicle, comprising: accessing a plurality of time-spaced image sequences depicting a region of a vehicle, captured by a plurality of image sensors positioned at a plurality of different views, identifying a plurality of candidate regions of damage in the plurality of time-spaced image sequences, performing a spatiotemporal correlation between the plurality of time-spaced image sequences, identifying redundancy in the plurality of candidate regions of damage corresponding to a common physical location of the vehicle denoting a single physical damage region, and providing an indication of the common physical location of the vehicle corresponding to the single physical damage region.
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/62 - Extraction of image or video features relating to a temporal dimension, e.g. time-based feature extractionPattern tracking
G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
19.
SYSTEMS AND METHODS FOR ONBOARD AUTO-INSPECTION OF VEHICLES USING REFLECTIVE SURFACES
Systems and methods for onboard auto-inspection of vehicles using reflective surfaces. The system comprises at least one imaging device mounted on the vehicle, capturing reflections of the vehicle's exterior from strategically positioned reflective surfaces. The captured imaging data is processed by one or more processing circuitries to detect and analyse defects or anomalies on the vehicle's exterior surface. The invention also describes a support system consisting of a scaffold with mounted reflective surfaces, easily integrable into existing infrastructure. The system employs advanced image analysis techniques to accurately detect and localize defects, generating a detailed defect map. The invention further encompasses a complete kit, including the onboard auto-inspection system and the support system, providing a comprehensive solution for vehicle inspection and maintenance. By enabling efficient, accurate, and frequent inspections, the present invention promotes safer, better-maintained vehicles and advances the field of automotive inspection technology.
G06T 7/70 - Determining position or orientation of objects or cameras
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
20.
Single pass automated vehicle inspection system and method
A vehicle inspection system that images and assesses an automobile or truck during a single passage through an inspection area. Undercarriage, tire, and body imaging assemblies activate at coordinated times to optimally capture photos detailing the vehicle underbody, wheel, and profile views. The system improves imaging precision through targeted illumination and sensor arrays tailored to respective inspection zones. A computational core synchronizes aggregate sensor output to amalgamate a comprehensive perspective of the vehicle with minimized throughput timing. Automated analysis then identifies any defects, wear, or damage across imaging clusters. The system facilitates expedited assessment to categorize large vehicle pool conditions via an integrated mechanics-free apparatus requiring only conventional operator access.
There is provided a computerized system comprising a processing unit and associated memory configured to obtain a three-dimensional dataset informative of at least part of a tread of a tire, and determine, using the three-dimensional dataset, data informative of tread depth of the tire.
A method for determining a fingerprint of a vehicle, including receiving a vehicle identifier and, from at least one sensor, at least one vehicle appearance each including image data informative of vehicle scan. The said appearance is associated with a unique appearance time tag. Then, segmenting the image data into segments each being informative of components of the vehicle. Then, determining marker instances from the image scan, wherein each marker instance is associated with a marker class and marker features. Then, storing data indicative of the vehicle's fingerprint including the vehicle identifier and its corresponding (i) vehicle appearance and associated appearance time tag, (ii) the so determined marker instances, thereby facilitating verification of the fingerprint of the vehicle in future vehicle scan(s).
There is provided a computerized system comprising a processing unit and associated memory configured to obtain a three-dimensional dataset informative of at least part of a tread of a tire, and determine, using the three-dimensional dataset, data informative of tread depth of the tire.
A method and system are provided for estimating tire tread depth, the method comprising: obtaining an image of the tire informative of tread and grooves embedded therein, wherein the image is acquired by an imaging device from a first angle relative to a horizontal direction perpendicular to tread surface, and the tire is illuminated by an illumination device from a second angle relative to the horizontal direction, causing a shadow section and an illuminated section at the bottom and/or sidewall of a groove, the first angle being smaller than the second angle, such that the image captures the illuminated section and at least part of the shadow section; performing segmentation on the image to obtain image segments corresponding to the illuminated section and the at least part of the shadow section; and estimating the tread depth based on the image segments, the groove width, and the second angle.
A method for determining a fingerprint of a vehicle, including receiving a vehicle identifier and, from at least one sensor, at least one vehicle appearance each including image data informative of vehicle scan. The said appearance is associated with a unique appearance time tag. Then, segmenting the image data into segments each being informative of components of the vehicle. Then, determining marker instances from the image scan, wherein each marker instance is associated with a marker class and marker features. Then, storing data indicative of the vehicle's fingerprint including the vehicle identifier and its corresponding (i) vehicle appearance and associated appearance time tag, (ii) the so determined marker instances, thereby facilitating verification of the fingerprint of the vehicle in future vehicle scan(s).
There is provided a computerized system comprising a processing unit and associated memory configured to obtain a three-dimensional dataset informative of at least part of a tread of a tire, and determine, using the three-dimensional dataset, data informative of tread depth of the tire.
A method and system are provided for estimating tire tread depth, the method comprising: obtaining an image of the tire informative of tread and grooves embedded therein, wherein the image is acquired by an imaging device from a first angle relative to a horizontal direction perpendicular to tread surface, and the tire is illuminated by an illumination device from a second angle relative to the horizontal direction, causing a shadow section and an illuminated section at the bottom and/or sidewall of a groove, the first angle being smaller than the second angle, such that the image captures the illuminated section and at least part of the shadow section; performing segmentation on the image to obtain image segments corresponding to the illuminated section and the at least part of the shadow section; and estimating the tread depth based on the image segments, the groove width, and the second angle.
There are provided a system and a method of automatic tire inspection, the method comprising: obtaining at least one image capturing a wheel of a vehicle; segmenting the at least one image into image segments including a tire image segment corresponding to a tire of the wheel; straightening the tire image segment from a curved shape to a straight shape, giving rise to a straight tire segment; identifying text marked on the tire from the straight tire segment, comprising: detecting locations of a plurality of text portions on the straight tire segment, and recognizing text content for each of the text portions; and analyzing the recognized text content based on one or more predefined rules indicative of association between text content of different text portions at given relative locations, giving rise to a text analysis result indicative of condition of the tire.
There is provided a method and system of depth map generation, including obtaining a pair of images during a relative movement between a vehicle and an imaging device; obtaining a segmentation map including one or more segments corresponding to one or more vehicle components; dividing the pair of images into one or more sections according to the segmentation map; and, for each given section, generating a depth map comprising: i) calculating a disparity map for the given section, the disparity map indicative of difference of location between each pixel in the given section in the first image and a matching pixel thereof in the second image, the matching pixel searched in a range defined within the same segment to which the pixel belongs; and ii) computing a depth map for the given section based on the disparity map.
G06T 7/593 - Depth or shape recovery from multiple images from stereo images
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
H04N 13/00 - Stereoscopic video systemsMulti-view video systemsDetails thereof
There is provided a method and system of depth map generation, including obtaining a pair of images during a relative movement between a vehicle and an imaging device; obtaining a segmentation map including one or more segments corresponding to one or more vehicle components; dividing the pair of images into one or more sections according to the segmentation map; and, for each given section, generating a depth map comprising: i) calculating a disparity map for the given section, the disparity map indicative of difference of location between each pixel in the given section in the first image and a matching pixel thereof in the second image, the matching pixel searched in a range defined within the same segment to which the pixel belongs; and ii) computing a depth map for the given section based on the disparity map.
There are provided a system and method of training a neural network system for anomaly detection, comprising: obtaining a training dataset including a set of original images and a set of random data vectors; constructing a neural network system comprising a generator, and a first discriminator and a second discriminator operatively connected to the generator; training the generator, the first discriminator and the second discriminator together based on the training dataset, such that: i) the generator is trained, at least based on evaluation of the first discriminator, to generate synthetic images meeting a criterion of photo-realism as compared to corresponding original images; and ii) the second discriminator is trained based on the original images and the synthetic images to discriminate images with anomaly from images without anomaly with a given level of accuracy, thereby giving rise to a trained neural network system.
A system configured for verification of imaged target detection performs the following: (a) receive information indicative of at least two images of object(s). This information comprises candidate target detection region(s), indicative of possible detection of a target associated with the object(s). Images have at least partial overlap. The candidate region(s) appears at least partially in the overlap area. The images are associated with different relative positions of capturing imaging device(s) and of an imaged portion of the object(s). (b) process the information to determine whether the candidate region(s) meets a detection repetition criterion, the criterion indicative of repetition of candidate region(s) in locations of the images that are associated with a same location on a data representation of the object(s). (c) if the criterion is met, classify the candidate region(s) as verified target detection region(s). This facilitates output of an indication of the verified region(s).
There are provided a method of vehicle inspection and a system thereof, the method comprising: obtaining a plurality of sets of images capturing a plurality of segments of surface of a vehicle at a plurality of time points; generating, for each time point, a 3D patch using a set of images capturing a corresponding segment at the time point, giving rise to a plurality of 3D patches; estimating 3D transformation of the plurality of 3D patches based on a relative movement between the imaging devices and the vehicle; and registering the plurality of 3D patches using the estimated 3D transformation thereby giving rise to a composite 3D point cloud of the vehicle. The composite 3D point cloud is usable for reconstructing a 3D mesh and/or 3D model of the vehicle where light reflection, comprised in at least some of the plurality of sets of images, is eliminated therefrom.
122,12,2133) if the split meets the convergence criterion, outputting data representative of a number of clusters as an estimation of the number of said one or more sources of sound.
G10L 25/30 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique using neural networks
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer hardware, sensors, downloadable computer software,
artificial intelligence software, downloadable cloud-based
software and mobile applications, all for connecting,
operating and managing networked devices for inspecting,
scanning, detecting, identifying and analyzing anomalies,
modifications or foreign objects in vehicles. Training in the use and operation of computer hardware,
sensors, downloadable and non-downloadable computer
software, downloadable and non-downloadable cloud-based
software, downloadable and non-downloadable artificial
intelligence software, mobile applications and software as a
service (SAAS), all for connecting, operating and managing
networked devices for inspecting, scanning, detecting,
identifying and analyzing anomalies, modifications or
foreign objects in vehicles. Providing temporary use of non-downloadable computer
software, non-downloadable cloud-based software,
non-downloadable artificial intelligence software and
software as a service (SAAS), all for connecting, operating
and managing networked devices for inspecting, scanning,
detecting, identifying and analyzing anomalies,
modifications or foreign objects in vehicles;
troubleshooting and technical support services of computer
software problems; technical support services, namely,
troubleshooting in the nature of computer hardware diagnosis
and diagnosing computer hardware problems; customized design
of computer hardware and software; programming, design,
development, installation, maintenance, repair, updating,
upgrading, leasing and rental of downloadable and
non-downloadable computer software, downloadable and
non-downloadable cloud-based software, downloadable and
non-downloadable artificial intelligence software, mobile
applications and software as a service (SAAS), all for
inspection, scanning, detecting, identifying and analyzing
anomalies, modifications or foreign objects in vehicles, and
consultancy services relating thereto; design, development,
leasing and rental of computer hardware and sensors, all for
inspection, scanning, detecting, identifying and analyzing
anomalies, modifications or foreign objects in vehicles.
36.
METHOD OF AUTOMATIC TIRE INSPECTION AND SYSTEM THEREOF
There are provided a system and a method of automatic tire inspection, the method comprising: obtaining at least one image capturing a wheel of a vehicle; segmenting the at least one image into image segments including a tire image segment corresponding to a tire of the wheel; straightening the tire image segment from a curved shape to a straight shape, giving rise to a straight tire segment; identifying text marked on the tire from the straight tire segment, comprising: detecting locations of a plurality of text portions on the straight tire segment, and recognizing text content for each of the text portions; and analyzing the recognized text content based on one or more predefined rules indicative of association between text content of different text portions at given relative locations, giving rise to a text analysis result indicative of condition of the tire.
There are provided a system and method of training a neural network system for anomaly detection, comprising: obtaining a training dataset including a set of original images and a set of random data vectors; constructing a neural network system comprising a generator, and a first discriminator and a second discriminator operatively connected to the generator; training the generator, the first discriminator and the second discriminator together based on the training dataset, such that: i) the generator is trained, at least based on evaluation of the first discriminator, to generate synthetic images meeting a criterion of photo-realism as compared to corresponding original images; and ii) the second discriminator is trained based on the original images and the synthetic images to discriminate images with anomaly from images without anomaly with a given level of accuracy, thereby giving rise to a trained neural network system.
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer hardware, RGB color sensors, laser sensors, proximity sensors, IR sensors, thermal sensors and sound sensors, downloadable computer software, recorded artificial intelligence software, downloadable cloud-based software and recorded mobile applications, all for connecting, operating and managing networked devices for inspecting, scanning, detecting, identifying and analyzing anomalies, modifications or foreign objects in vehicles Training in the use and operation of computer hardware, sensors, downloadable and non-downloadable computer software, downloadable and non-downloadable cloud-based software, downloadable and non-downloadable artificial intelligence software, mobile applications and software as a service (SAAS), all for connecting, operating and managing networked devices for inspecting, scanning, detecting, identifying and analyzing anomalies, modifications or foreign objects in vehicles Providing temporary use of non-downloadable computer software, non-downloadable cloud-based software, non-downloadable artificial intelligence software and software as a service (SAAS), all for connecting, operating and managing networked devices for inspecting, scanning, detecting, identifying and analyzing anomalies, modifications or foreign objects in vehicles; troubleshooting and technical support services of computer software problems; technical support services, namely, troubleshooting in the nature of computer hardware diagnosis and diagnosing computer hardware problems; customized design of computer hardware and software; programming, design, development, installation, maintenance, repair, updating, upgrading, leasing and rental of downloadable and non-downloadable computer software, downloadable and non-downloadable cloud-based software, downloadable and non-downloadable artificial intelligence software, mobile applications and software as a service (SAAS), all for inspection, scanning, detecting, identifying and analyzing anomalies, modifications or foreign objects in vehicles, and consultancy services relating thereto; design, development, leasing and rental of computer hardware and sensors, all for inspection, scanning, detecting, identifying and analyzing anomalies, modifications or foreign objects in vehicles
39.
Method of vehicle image comparison and system thereof
There are provided a system and method of vehicle image comparison, the method including: obtaining an input image comprising a plurality of image portions; retrieving a set of reference images; for each image portion, searching for a best matching reference portion in the set of reference images, comprising: i) for each given reference image: identifying a reference region; using a similarity model on the given image portion and the reference region to obtain a similarity map indicating a similarity between the image portion and a respective reference image portion; and selecting a reference image portion with the best similarity as a reference portion candidate; and ii) selecting the best matching reference portion; and comparing each given image portion with the best matching reference portion using a comparison model, giving rise to a difference map indicating probability of presence of DOI in the given image portion.
There are provided a system and method of vehicle image comparison, the method including: obtaining an input image comprising a plurality of image portions; retrieving a set of reference images; for each image portion, searching for a best matching reference portion in the set of reference images, comprising: i) for each given reference image: identifying a reference region; using a similarity model on the given image portion and the reference region to obtain a similarity map indicating a similarity between the image portion and a respective reference image portion; and selecting a reference image portion with the best similarity as a reference portion candidate; and ii) selecting the best matching reference portion; and comparing each given image portion with the best matching reference portion using a comparison model, giving rise to a difference map indicating probability of presence of DOI in the given image portion.
G06T 3/00 - Geometric image transformations in the plane of the image
G06K 9/66 - Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
41.
METHOD OF VEHICLE IMAGE COMPARISON AND SYSTEM THEREOF
There are provided a system and method of vehicle image comparison, the method including: obtaining an input image capturing at least part of a vehicle; segmenting the input image into one or more input segments corresponding to one or more mechanical components; retrieving a set of reference images, thereby obtaining a respective set of corresponding reference segments for each input segment; and generating at least one difference map corresponding to at least one input segment, comprising, for each input segment: comparing the input segment with each corresponding reference segment thereof using a comparison model, giving rise to a set of difference map candidates each indicating probability of presence of DOI between the given input segment and the corresponding reference segment; and providing a difference map corresponding to the given input segment according to probability of each difference map candidate in the set of difference map candidates.
G06K 9/46 - Extraction of features or characteristics of the image
G06K 9/66 - Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
There are provided a system and method of vehicle image comparison, the method including: obtaining an input image capturing at least part of a vehicle; segmenting the input image into one or more input segments corresponding to one or more mechanical components; retrieving a set of reference images, thereby obtaining a respective set of corresponding reference segments for each input segment; and generating at least one difference map corresponding to at least one input segment, comprising, for each input segment: comparing the input segment with each corresponding reference segment thereof using a comparison model, giving rise to a set of difference map candidates each indicating probability of presence of DOI between the given input segment and the corresponding reference segment; and providing a difference map corresponding to the given input segment according to probability of each difference map candidate in the set of difference map candidates.
There are provided a method of vehicle inspection and a system thereof, the method comprising: obtaining a plurality of sets of images capturing a plurality of segments of surface of a vehicle at a plurality of time points; generating, for each time point, a 3D patch using a set of images capturing a corresponding segment at the time point, giving rise to a plurality of 3D patches; estimating 3D transformation of the plurality of 3D patches based on a relative movement between the imaging devices and the vehicle; and registering the plurality of 3D patches using the estimated 3D transformation thereby giving rise to a composite 3D point cloud of the vehicle. The composite 3D point cloud is usable for reconstructing a 3D mesh and/or 3D model of the vehicle where light reflection, comprised in at least some of the plurality of sets of images, is eliminated therefrom.
