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: 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 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: 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: 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 for estimating an information unit represented by DNA strands, the method includes (a) sequencing the DNA strands to provide noisy copies of an encoded version of the information unit; wherein the information unit comprises information unit elements; (b) neural network (NN) processing the multiple noisy copies by one or more NNs to provide a soft estimate of the encoded information unit; wherein the soft estimate comprises estimated encoded information unit elements and an encoded information unit elements estimated confidence parameter; and (c) decoding the soft estimate of the encoded information unit to provide a prediction of the information unit.

Abstract: A method and system for segmenting free space area, in real-time, based on data from an electromagnetic device may include: an image acquisition device adapted to capture an image of a scene; an electromagnetic device adapted to produce data corresponding to the scene; a non-transitory memory device, wherein modules of instruction code may be stored; and a processor, associated with the memory device, and configured to execute the modules of instruction code. Upon execution of said modules of instruction code, the processor may be configured to train a module to perceive elements in a scene.

Abstract: A system and method for improving a resolution of a system may include providing to the ML module a set of input electromagnetic signals from an array included in a system; and improving, by the ML module, the resolution of the system by generating and providing at least one additional electromagnetic signal, based on the received set.

Abstract: A method and system for segmenting free space area, in real-time, based on data from an electromagnetic device may include: an image acquisition device adapted to capture an image of a scene; an electromagnetic device adapted to produce data corresponding to the scene; a non-transitory memory device, wherein modules of instruction code may be stored; and a processor, associated with the memory device, and configured to execute the modules of instruction code. Upon execution of said modules of instruction code, the processor may be configured to train a module to perceive elements in a scene.

Abstract: A system and a method for training a machine learning (ML) model to enhance a performance of an electromagnetic (EM) sensor, the method including: receiving one or more first data elements pertaining to a first signal of an EM sensor having a first performance parameter value; receiving one or more second data elements pertaining to a second signal of a data generator having a second performance parameter value; and training the ML model to generate a third signal, using the one or more first data elements as a training data set and using the one or more second data elements as supervisory data, where the third signal is characterized by a third performance parameter value that is higher than the first performance parameter value.

Abstract: A method and a system for training a machine-learning model to identify real-world elements using a simulated environment (SE) may include (a) receiving at least one set of appearance parameters, corresponding to appearance of real-world element; (b) generating one or more realistic elements, each corresponding to a variant of at least one real-world element; (c) generating one or more abstract-elements; (d) placing the elements within the SE; (e) producing at least one synthetic image from the SE; (f) providing the at least one synthetic image to a machine-learning model; and (g) training the machine-learning model to identify at least one real-world element from the at least one synthetic image, that corresponds to at least one realistic element in the SE.