Systems and methods for detecting hail damage on a vehicle are described including, receiving an image of at least a section of a vehicle. Detecting a plurality of hail damage including, detecting a plurality of damaged areas distributed over the entire section of the vehicle, and differentiating the plurality of damaged areas from one or more areas of noise, processing the received image to classify one or more sections of the vehicle as one or more panels of the vehicle bodywork, and using the detected areas of damage, the classification of the seriousness of the damage and the classification of one or more panels to compute a panel damage density estimate.

Disclosed are a method of inspecting and evaluating a coating state of a steel structure, and a system therefor. A plurality of vision images and thermal images are acquired. While acquiring the thermal images, a desired region is heated. After the thermal images and the vision images in a dynamic state are reconstructed into a time-spatial-integrated thermal image and a time-spatial-integrated vision image in a static state, respectively, an overlay image is generated by overlaying the two images. A deterioration region of a coating is detected, and coating deterioration is classified by characteristics. A size of the coating deterioration region is quantified. A thickness of the coating is inspected by analyzing thermal energy measured from the time-spatial-integrated thermal image. A coating grade is calculated by comprehensively evaluating a coating deterioration inspection result and a coating thickness inspection result. A state evaluation report for the steel structure is automatically created.

Disclosed herein are a surface defect detection method and apparatus. The surface defect detection method is performed by the surface defect detection apparatus. The surface defect detection method includes: acquiring a photographed image of an inspection target object; and detecting a defect by using the acquired photographed image. Acquiring the photographed image includes radiating pattern light of a stripe pattern having a predetermined interval onto a surface of the inspection target object, and acquiring a photographed image by photographing reflected light reflected from the surface of the inspection target object.

The present disclosure provides information about a coating history of a vehicle. An information processing apparatus executes acquiring, for a vehicle with a later peelable second coating film formed being layered on at least a part of a first coating film, history data that is data about work on the second coating film, and acquiring a first image that is an external appearance image of the vehicle before the second coating film being formed, based on the history data.

An inspection device includes: a captured image acquisition unit configured to acquire a captured image obtained by capturing an image of an inspection target; an image division unit configured to divide the captured image which is acquired into a plurality of first division images; a first composite image generation unit configured to superimpose the plurality of first division images to generate a composite image; and a determination unit configured to use the generated composite image and a learning model to determine whether or not a defect is present in the inspection target.

The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether each part of a vehicle should be classified as damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle. Aspects and/or embodiments seek to provide a computer-implemented method for determining damage states of each part of a damaged vehicle, indicating whether each part of the vehicle is damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle, using images of the damage to the vehicle and trained models to assess the damage indicated in the images of the damaged vehicle.

Techniques for identifying pixel groups representing objects in an image include using images having multiple groups of pixels, grouped such that each pixel group represents a zone of interest and determining a pixel value for pixels within each pixel group based on a comparison of pixel values for each individual pixel within the group. A probability heat map is derived from the pixel group values using a first neural network using the pixel group values as input and produces the heat map having a set of graded values indicative of the probability that the respective pixel group includes an object of interest. A zone of interest is identified based on whether the groups of graded values meet a determined probability threshold objects of interest are identified within the at least one zone of interest by way of a second neural network.

Systems and methods for detecting surface anomalies are disclosed. For example, a computer-implemented method for detecting surface anomalies on an object comprises receiving measured electromagnetic radiation (EMR) profiles for the object, generating synthetic EMR profiles for the object based on the measured EMR profiles, determining whether the object contains a surface anomaly based on the measured EMR profiles and the synthetic EMR profiles, and indicating a surface anomaly to a user via a display when a surface anomaly is detected. In another example, a system comprises a computing device comprising non-transitory memory with computer-readable instructions for receiving unpaired image data of an object of two different image types, predicting missing image data to generate paired image data of the two different image types, and determining whether the object contains a surface anomaly based on the paired image data. The computing device comprises a processor configured to execute the computer-readable instructions.

Aspects described herein may facilitate an automated trade-in of a vehicle with limited human interaction. A server may receive a request to begin a value determination of a vehicle associated with the user. The server may receive first data comprising: vehicle-specific identifying information, and multimedia content showing a first aspect of the vehicle. The user may be directed to place the vehicle within a predetermined staging area. The server may receive, from one or more image sensors associated with the staging area, second data comprising multimedia content showing a second aspect of the vehicle. The server may create a feature vector comprising the first data and the second data. The feature vector may be inputted into a machine learning algorithm corresponding to the vehicle-specific identifying information of the vehicle. Based on the machine learning algorithm, the server may determine a value of the vehicle.

The present invention relates to verification of damage to vehicles. More particularly, the present invention relates to a universal approach to automated generation of a damage estimate to a vehicle using images of the vehicle and verification of a manually-generated damage repair proposals using the automatically generated damage estimate.

Aspects and/or embodiments seek to provide a computer-implemented method of generating one or more repair estimates from one or more photos of a damaged vehicle and comparing the generated estimate(s) to one or more input repair estimates to verify the one or more input repair estimates.