A global and local binary pattern image crack segmentation method based on robot vision comprises the following steps: enhancing a contrast of an acquired original image to obtain an enhanced map; using an improved local binary pattern detection algorithm to process the enhanced map and construct a saliency map; using the enhanced map and the saliency map to segment cracks and obtaining a global and local binary pattern automatic crack segmentation method; and evaluating performance of the obtained global and local binary pattern automatic crack segmentation method. The present application uses logarithmic transformation to enhance the contrast of a crack image, so that information of dark parts of the cracks is richer. Texture features of a rotation invariant local binary pattern are improved. Global information of four directions is integrated, and the law of universal gravitation and gray and roundness features are introduced to correct crack segmentation results, thereby improving segmentation accuracy. Crack regions can be segmented in the background of uneven illumination and complex textures. The method has good robustness and meets requirements of online detection.

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.

Methods and systems for performing diagnostic functions for a deep learning model are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a deep learning model configured for determining information from an image generated for a specimen by an imaging tool. The one or more components also include a diagnostic component configured for determining one or more causal portions of the image that resulted in the information being determined and for performing one or more functions based on the determined one or more causal portions of the image.

Systems and methods for automatically grading a user device are provided. Such systems and methods can include (1) a lighting element positioned at an angle relative to a platform, (2) an imaging device positioned at the angle relative to the platform such that light emitted from the lighting element and a field of view of the imaging device form a right angle where the light emitted from the lighting element and the field of view meet at a user device when the user device is positioned at a predetermined location on the platform, and (3) control circuitry that can activate the lighting element, instruct the imaging device to capture an image of a screen of the user device while the user device is at the predetermined location and is being illuminated by the first lighting element, and parse the image to determine whether the screen is damaged.

One example of an inspection system includes a laser, a magnification changer, and a first camera. The laser projects a line onto a wafer to be inspected. The magnification changer includes a plurality of selectable lenses of different magnification. The first camera images the line projected onto the wafer and outputs three-dimensional line data indicating the height of features of the wafer. Each lens of the magnification changer provides the same nominal focal plane position of the first camera with respect to the wafer.

A computer-implemented method comprising: receiving data comprising two-dimensional data and three-dimensional data of a component of an engine; identifying a feature of the component using the two-dimensional data; determining coordinates of the feature in the two-dimensional data; determining coordinates of the feature in the three-dimensional data using: the determined coordinates of the feature in the two-dimensional data; and a pre-determined transformation between coordinates in two-dimensional data and coordinates in three-dimensional data; and measuring a parameter of the feature of the component using the determined coordinates of the feature in the three-dimensional data.

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.

A measurement system is provided for predicting a future status of a refractory lining that is lined over an inner surface of an outer wall of a manufacturing vessel and exposed to an operational cycle during which the refractory lining is exposed to a high-temperature environment for producing a non-metal and the produced non-metal. The system includes one or more laser scanners and a processor. The laser scanners are configured to conduct one or more pre-operational laser scans of the refractory lining prior to the operational cycle to collect data related to pre-operational cycle structural conditions, and one or more post-operational laser scans of the refractory lining after the operational cycle to collect data related to post-operational cycle structural conditions of the refractory lining. The processor is configured to predict future status of the refractory lining after subsequent operational cycles based on the determined exposure impact of the operational cycle.

A calibrated three-dimensional optical measuring apparatus for the three-dimensional measurement of geometric parameters of a rope has a frame defining and arranged around a rope receiving cavity. A plurality of image acquisition devices is configured to acquire a plurality of digital images of at least one region of an outer surface of the rope. The image acquisition devices are fixed to the frame and arranged around the rope when the calibrated three-dimensional optical measuring apparatus receives the rope in the rope receiving cavity. An attachment device is configured to constrain the calibrated three-dimensional optical measuring apparatus to the rope in a relatively translatable manner with respect to the rope. An electronic digital image processing device is configured to process a multiplicity of digital images and obtain a three-dimensional photogrammetric reconstruction of points of the digital images of the rope acquired by the image acquisition devices.