A method for quantifying an exposure dose for a surface is disclosed. The method may include emitting one or more beams of 222 nm light onto a portion of the surface using one or more far ultraviolet (UV) light sources capable of emitting 222 nm light, the portion of the surface being coated with one or more fluorescent coatings. The method may include capturing images of the portion of the surface. The method may include adjusting one or more image characteristics for the captured images using one or more filtering methods. The method may include generating a histogram of the adjusted images based on the one or more filtering methods. The method may include determining a pixel surface area for the generated histogram. The method may include calculating the exposure dose for the surface based on the generated pixel surface area and a predetermined calibration curve.

Provided is an image and an inspection system that make it possible to effectively utilize a result of past inspection. An audit position indicating a position where an audit is planned or a position where an audit is completed and a past audit position indicating a position where an audit was performed in the past within a predetermined range including the audit position are displayed over a map on a user terminal, the map being a drawing representing a configuration of a manufacturing site. The inspector references the map on the user terminal and effectively utilizes a past audit result to perform an audit.

A system for imaging and analyzing a vehicle may include a frame having a central passage, wherein the central passage is configured and dimensioned to allow a vehicle to pass through. The frame may include, for example, a pair of substantially vertical legs connected at the top by a cross member, wherein the legs and cross member define the central passage. One or more bollards may be positioned in front of and/or behind the frame. A plurality of cameras within the each leg, cross member, and/or bollard may be directed toward the passage to record video images of a passing vehicle. Integrated LED array panels may provide bands of light to aid in detection of surface anomalies, for example by simultaneous analysis of symmetrical sides of the 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.

System and methods for matching color and appearance of a target coating are provided herein. The system includes an electronic imaging device configured to receive a target image data of the target coating. The target image data includes target coating features. The system further includes one or more feature extraction algorithms that extracts the target image features from the target image data. The system further includes a machine-learning model that identifies a calculated match sample image from a plurality of sample images utilizing the target image features. The machine-learning model includes pre-specified matching criteria representing the plurality of sample images for identifying the calculated match sample image from the plurality of sample images. The calculated match sample image is utilized for matching color and appearance of the target coating.

A method of locating a defect in an e-coat on a surface can include acquiring an image of the surface. A correction coefficient can be applied to the image to form an adjusted image. The correction coefficient can relate pixel values of the image to a calibration value. The adjusted image can be separated into a spectral component which can be modified by a block average determination to create a modified spectral component. The spectral components can be compared with the modified spectral components to form a difference image. The difference image can be dilated and eroded. A region of interest can be identified from an image region using a blob detection. The defect can be classified as a defect type. The defect can be repaired or a coding parameter can be altered based on the defect.

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.

A system may receive video of a damaged vehicle, perform image analysis of the video to determine one or more frames of the video that include a damaged portion of the vehicle, further analyze the one or more frames of the video that include a damaged portion of the vehicle to determine a damaged cluster of parts of the vehicle, determine whether the damaged cluster of parts should be repaired or replaced, map the damaged cluster of parts to one or more parts in a vehicle-specific database of parts, and generate, based on the mapping, a list of parts for repair or replacement.

Described herein is a computer-implemented method. The method includes: providing digital images and respective formulas for coating compositions with known pigments and/or pigment classes associated with the respective digital images, classifying, using an image annotation tool, for each digital image, each pixel, by visually reviewing the respective digital image pixel-wise, providing, for each digital image, an associated pixel-wise annotated image, training a first neural network with the provided digital images as input and the associated pixel-wise annotated images as output, making the trained first neural network available for applying the trained first neural network to at least one unknown input image of a target coating and for assigning a pigment label and/or a pigment class label to each pixel in the at least one unknown input image, and determining and/or outputting, for each unknown input image, a statistic of corresponding identified pigments and/or pigment classes, respectively.

According to the present disclosure, there is provided a coating determining device of a coating head that can prevent coating defect caused by clogging in multiple successive coatings. The coating determining device according to the present disclosure can determine a coating state of the coating head, wherein the coating head having a plurality of nozzles for ejecting paint, the plurality of nozzles being configured to eject the paint out of the nozzles so as to coat a workpiece with the ejected paint while moving in a direction, the coating determining device of the coating head being characterized by comprising: an image acquiring unit for acquiring an image of the coated workpiece; and a determination unit for determining, based on the image acquired by the image acquiring unit, whether coating the workpiece is implemented normally.