Systems and methods are provided for detecting surface aberrations on a vehicle. The surface aberrations may not be readily apparent to the human eye. Machine learning constructs image detecting systems to automate the detection of surface aberrations on the vehicle. Additionally, the machine learning may refine the aberration detection systems to identify the cause of the surface aberration on the vehicle and enhance the capabilities of the aberration detection systems.

An inspection system includes an acquisition unit and a determination unit. The acquisition unit acquires an image representing a surface of an object. The determination unit performs color determination processing. The color determination processing is performed to determine a color of the surface of the object based on a plurality of conditions of reflection. The plurality of conditions of reflection are obtained from the image representing the surface of the object as acquired by the acquisition unit, and have a specular reflection component and a diffuse reflection component at respectively different ratios on the surface of the object.

To provide a coating diagnosis system and a coating diagnosis method that allow centralized management over data related to processes in a painting line, including a pretreatment process. The coating diagnosis system includes a data acquisition unit of a pretreatment controller that acquires data related to a pretreatment process when pretreatment for paint is performed on a workpiece in the pretreatment process in a painting line, and a data management unit of a host controller that registers the data related to the pretreatment process in a database in associated with a serial number of the workpiece.

An inspection system is provided for detecting defects on surfaces. The system uses a pattern with varying color or darkness which faces the surface. A light illuminates the pattern on the surface so that the pattern and any defects on the surface are reflected and captured for image analysis. The processor then separates the pattern from the image in order to identify the locations of any defects on the surface.

A system for capturing VIN numbers and vehicles images to track vehicle damage through vehicle supply chains which includes a mobile software application and/or robot(s) which moves autonomously around parking lots. The mobile application can direct the user to capture VIN images and/or vehicle images from certain views and collect GPS positions of the same and the robot includes various cameras and sensors to identify vehicles and take pictures of them. All of the captured images of vehicles are sent to a central server/storage where the vehicle images can be checked for damage as compared to locations so that it can be determined who was in possession of the vehicle when damage occurred.

An airflow detection apparatus (1) an airflow detection apparatus (1) that detects an airflow in a painting booth, and includes a thread-like member (2) installed inside the painting booth, an image capture device (4) capable of generating image data obtained by capturing an image of the thread-like member (2), and a calculation device capable of performing calculation processing on the image data. The thread-like member (2) is installed in the painting booth in a mode in which at least a portion thereof is suspended. The calculation device specifies a portion corresponding to the thread-like member (2) in the image data, and detects an airflow based on a position of the portion.

An inspection system is provided for detecting defects on surfaces. The system uses a pattern with varying color or darkness which faces the surface. A light illuminates the pattern on the surface so that the pattern and any defects on the surface are reflected and captured for image analysis. The processor then separates the pattern from the image in order to identify the locations of any defects on the surface.

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.

Disclosed is a vehicle protection fence repair plating system and method using artificial intelligence. The system includes a data management module that collects video data about a vehicle protection fence and pre-processes images per frame, a data prediction module that receives the data of the pre-processed image and performs machine learning for a corrosion level of the vehicle protection fence according to a preset labeling standard to detect a work area, and a process management module that standardizes customized work instructions according to a determination result of an image state of the vehicle protection fence, which has been machine-learned, wherein the data prediction module specifies a repair range of the vehicle protection fence and a work method for each repair range according to the labeling standard.

A system and method are provided for automatically estimating a repair cost for a vehicle. A method includes: receiving, at a server computing device over an electronic network, one or more images of a damaged vehicle from a client computing device; performing image processing operations on each of the one or more images to detect external damage to a first set of parts of the vehicle; inferring internal damage to a second set of parts of the vehicle based on the detected external damage; and, calculating an estimated repair cost for the vehicle based on the detected external damage and inferred internal damage based on accessing a parts database that includes repair and labor costs for each part in the first and second sets of parts.