The present invention relates to a device (1) for detecting surface defects in an object (100), for example an industrial gasket. The detection device comprises lighting means (2) configured to illuminate said object with a first light radiation (L.sub.1) having a first lighting direction (D.sub.1) or with a second light radiation (L.sub.2) having a second lighting direction (D.sub.2). According to the invention, the detection device comprises acquisition means (30) configured to acquire a plurality of B/W images of said object, when illuminated by said lighting means.

A method of detecting defects in a composite layup includes capturing, using an infrared camera, reference images of a reference layup being laid up by a reference layup head. The method also includes manually reviewing the reference images for defects, and generating reference defect masks indicating defects in the reference images. The method further includes training, using the reference images and reference defect masks, a neural network, creating a machine learning model that, given a production image as input, outputs a production defect mask indicating the defect location and the defect type of each defect. The method also includes capturing, using an infrared camera, production images of a production layup being laid up by the production layup head, and applying the model to the production images to automatically generate a production defect masks indicating each defect in the production images.

A part inspection system includes a vision device configured to image a part being inspected and generate a digital image of the part. The system includes a part inspection module communicatively coupled to the vision device and receives the digital image of the part as an input image. The part inspection module includes a defect detection model. The defect detection model includes a template image. The defect detection model compares the input image to the template image to identify defects. The defect detection model generates an output image. The defect detection model configured to overlay defect identifiers on the output image at the identified defect locations, if any.

A coating analyzer is configured to receive electronic image data of a physical coating and to generate information regarding the pigments of the physical coating. The coating analyzer applies a computer vision model trained on baseline image data to the electronic image data. The coating analyzer assigns color values to the pigments forming the electronic image data and generates pigment groups based on the assigned color values. The pigment groups provide color palette data regarding the pigments forming the coating.

A deep learning-based method for calculating an overhang of a battery includes the following steps: obtaining a training sample image set; training a neural network according to the training sample image set to obtain a segmentation network model; detecting an object detection image of the battery to be detected according to the segmentation network model to obtain a corresponding first binarized image; obtaining top coordinates of each of a positive electrode and a negative electrode of the battery to be detected according to the first binarized image; and calculating the overhang of the battery to be detected according to the top coordinates.

Embodiments relate to a method and system for determining a situation of a facility by imaging a sensing data of the facility including receiving sensing data through a plurality of sensors at a query time, generating a situation image at the query time, showing the situation of the facility at the query time based on the sensing data, and determining if an abnormal situation occurred at the query time by applying the situation image to a pre-learned situation determination model.

Dynamic thin film interferometry is a technique used to non-invasively characterize the thickness of thin liquid films that are evolving in both space and time. Recovering the underlying thickness from the captured interferograms, unconditionally and automatically is still an open problem. A compact setup is provided employing a snapshot hyperspectral camera and the related algorithms for the automated determination of thickness profiles of dynamic thin liquid films. The technique is shown to recover film thickness profiles to within 100 nm of accuracy as compared to those profiles reconstructed through the manual color matching process. Characteristics and advantages of hyperspectral interferometry are discussed including the increased robustness against imaging noise as well as the ability to perform thickness reconstruction without considering the absolute light intensity information.

Methods, apparatus, systems and articles of manufacture are disclosed for augmented reality vehicle condition inspection. An example apparatus disclosed herein includes a location analyzer to determine whether a camera is at an inspection location and directed towards a first vehicle in an inspection profile, the inspection location corresponding to a location of the camera relative to the first vehicle, an interface generator to generate an indication on a display that the camera is at the inspection location, the indication associated with an inspection image being captured, and an image analyzer to compare the inspection image captured at the inspection location with a reference image taken of a reference vehicle of a same type as the first vehicle, and determine a vehicle part condition or a vehicle condition based on the comparison of the inspection image and the reference image.

An information processing apparatus (10) is for supporting work by a user who uses drawings for a plant. The information processing apparatus (10) includes a controller (15). The controller (15) is configured to convert a drawing including elements configuring the plant into an abstract model represented by element information indicating the elements and connection information indicating a connection relationship between the elements. The controller (15) is configured to generate display information, when it is judged that a difference exists between one abstract model based on one drawing and another abstract model based on another drawing, for displaying the differing portion in a different form than another portion.

The disclosure provides an internal thermal fault diagnosing method for an oil-immersed transformer based on DCNN and image segmentation, including: 1) dividing an internal area of a transformer, and using fault areas and normal status as labels of DCNN; 2) through lattice Boltzmann simulation, randomly obtaining multiple feature images of the internal temperature field distribution of the transformer under normal and various fault state modes, and the fault area serves as a label to form the underlying training sample set; 3) obtaining historical monitoring information of the infrared camera or temperature sensor, and forming its corresponding fault diagnosis results into labels; 4) combining all monitoring information contained in each sample into one image, and then extracting the same monitoring information from the samples in the sample set to form a new image; 5) segmenting image sample and then inputting the same into DCNN for training to obtain diagnosis results.