Abstract: There is provided a method, comprising: feeding an input image of a vehicle into an inspection AI-agent for generating reference data including vehicle parts and visual features, feeding the input image and the reference data into a generation AI-agent for generating a standardized image rendering the vehicle according to standardized parameters, in iterations: feeding the standardized image into the inspection AI-agent for evaluating the standardized image in view of the input image for detecting visual inconsistencies, adapting the reference data for correcting the visual inconsistencies, feeding the adapted reference data into the generation AI-agent to obtain an adaptation of the standardized image, wherein the adaptation of the standardized image is fed into the inspection AI-agent in a subsequent iteration, wherein the iterations are terminated when the detected inconsistencies meet a requirement denoting visually insignificant differences that preserve visual identity of the specific physical vehicle i

Abstract: A vehicle inspection system consolidates redundant multi-view defect detections using three-dimensional spatial clustering on CAD models. The system projects two-dimensional defect detections from multiple camera viewpoints onto three-dimensional CAD model surfaces through ray-casting algorithms, then clusters projected locations based on geodesic distances to identify unique physical defects. This approach eliminates detection redundancy while providing precise spatial localization, enabling consistent defect tracking across multiple inspections regardless of vehicle positioning variations.

Abstract: There is provided a system for non-destructive contact-less evaluation of an electric vehicle (EV) battery of an electric vehicle, comprising: an electromagnetic (EM) transducer(s), an acoustic transducer(s), a thermal camera(s), and a processor(s) configured for: operating the EM transducer(s) and the acoustic transducer(s) for generating EM radiation and acoustic energy towards the EV battery, operating the EM transducer(s), and the acoustic transducer(s) for intercepting EM radiation and acoustic energy reflected off the EV battery and/or passing through the EV battery, operating the thermal camera(s) for capturing a thermal image(s) of the vehicle, extracting features from the intercepted EM radiation, the acoustic energy, and the thermal image(s), generating a multi-dimensional dataset by aggregating the features, feeding the multi-dimensional dataset into a machine learning model, and obtaining from the machine learning model, an indication of an anomaly in the EV battery.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 sing

Abstract: 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.

Abstract: 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 sing

Abstract: 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.