A strain measurement method includes disposing a 3D camera module at a first measurement position; using the 3D camera module to acquire a first 3D image of a to-be-measured object at a first to-be-measured position; acquiring a second 3D image of the to-be-measured object at the first to-be-measured position; and splicing the first and second 3D images to obtain an initial 3D image. The method still includes: moving the 3D camera module from the first measurement position to a second measurement position; using the 3D camera module to acquire a third 3D image of the to-be-measured object at a second to-be-measured position; acquiring a fourth 3D image of the to-be-measured object at the second to-be-measured position; and splicing the third and fourth 3D images to obtain a deformed 3D image. The method further includes comparing the initial 3D image and the deformed 3D image to output 3D deformation information.

Current inspection processes employed for pipeline networks data acquisition aided with manually locating and recording defects/observations, thus leading labor intensive, prone to error and a time-consuming task thereby resulting in process inefficiencies. Embodiments of the present disclosure provide systems and methods for that leverage artificial intelligence/machine learning models and image processing techniques to automate log and data processing, reports and insights generation thereby reduce dependency on manual analysis, improve annual productivity of survey meterage and bring in process and cost efficiencies into overall asset health management for utilities, thereby enhancing accuracy in defect identification, analysis, classification thereof.

A data processing method for detection of deterioration of semiconductor process kits includes the following steps: acquiring a plurality of Raman spectra data of a semiconductor process kit and performing a plurality calculating processes on the Raman spectra data to obtain a first deterioration state determining parameter indicating the aging degree of the entire semiconductor process kit and a second deterioration state determining parameter indicating the degree of variation of the internal molecular structure of the semiconductor process kit.

A method of assessing damage to a component includes displaying a sensor image of the component in a first viewing pane, displaying a reference image of the component, which is a graphical depiction of the component with accurate dimensions, in a second viewing pane, placing a plurality of first identification markers on the sensor image of the component in the first viewing pane to correspond to a matching location with a second identification marker on the component in the reference image, identifying a region of damage on the component in the sensor image, mapping the region of damage to the component in the reference image using the plurality of first and second identification markers, and calculating a size of the region of damage.

Various defects in an electronic assembly can be intelligently identified with a system having at least a server connected to a first capture module and a second capture module. The first capture module may be positioned proximal a first manufacturing line while the second capture module is positioned proximal a second manufacturing line. Images can be collected of first and second electronic assemblies by respective first and second capture modules prior to the images being sent to a classification module of the server where at least one defect is automatically detected in each of the first and second electronic assemblies concurrently with the classification module.

In one example, a distance sensor includes a camera to capture images of a field of view, a plurality of light sources arranged around a lens of the camera, wherein each light source of the plurality of light sources is configured to project a plurality of beams of light into the field of view, and wherein the plurality of beams of light creates a pattern of projection artifacts in the field of view that is visible to a detector of the camera, a baffle attached to a first light source of the plurality of light sources, wherein the baffle is positioned to limit a fan angle of a plurality of beams of light that is projected by the first light source, and a processing system to calculate a distance from the distance sensor to an object in the field of view, based on an analysis of the images.

Fluid leak observations made by different types of sensors are combined to detect fluid leaks at a fluid facility. Separate fluid leak observations made by different types of sensors are reconciled using a Bayesian model. The Bayesian model outputs likelihoods of different fluid leak probabilities, and the likelihoods of different fluid leak probabilities are used to facilitate operations at the fluid facility.

A method of assessing damage to a component includes displaying a sensor image of the component in a first viewing pane, displaying a reference image of the component, which is a graphical depiction of the component with accurate dimensions, in a second viewing pane, placing a plurality of first identification markers on the sensor image of the component in the first viewing pane to correspond to a matching location with a second identification marker on the component in the reference image, identifying a region of damage on the component in the sensor image, mapping the region of damage to the component in the reference image using the plurality of first and second identification markers, and calculating a size of the region of damage.

Methods and systems for generating defect samples are provided. One method includes identifying a set of defects detected on a wafer having the most diversity in values of at least one defect attribute and generating different tiles for different defects in the set. The tiles define a portion of all values for the at least one attribute of all defects detected on the wafer that are closer to the values for the at least one attribute of their corresponding defects than the values for the at least one attribute of other defects. In addition, the method includes separating the defects on the wafer into sample bins corresponding to the different tiles based on their values of the at least one attribute, randomly selecting defect(s) from each of two or more of the sample bins, and creating a defect sample for the wafer that includes the randomly selected defects.

An inspection device includes an input portion and a determining portion. The input portion is configured to receive an input of an image taken of an object. The determining portion is configured to execute a first process on each of a plurality of inspection regions including a first inspection region and a second inspection region. The first process is a process relating to a determination as to quality of the object based on the image. The first inspection region includes a specific region not included in an inspection region other than the first inspection region of the plurality of inspection regions. The determining portion is configured to execute a second process. The second process is a process of determining, based on a result of the first process executed on each of the plurality of inspection regions, the quality of the object.