Uveye Ltd.

Ohad Hever

Amir Hever

Ilya Bogomolny

Ilya Grinshpoun

Dvir Paravi

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.

Single pass automated vehicle inspection system and method

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).

Imaged target detection verification

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.

Method of automatic tire inspection and system thereof

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.

Method of anomaly detection and system thereof

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.

System of vehicle inspection and method 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.

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.

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Modiin, Israel

Ohad Hever