A system for taking high-resolution photographs from a vehicle-mounted camera, forming orthomosaics from video and/or multiple high-resolution photographs, and using artificial intelligence to detect and classify pavement flaws and defects in the imagery. Detection also includes the ability to capture quantifiable metrics for the defects and/or a region of interest. Three-dimensional imagery is produced from the same images as the orthomosaics. Surface and terrain map products made from the same source images capture additional details such as depth and volume. The highlighted orthomosaics and three-dimensional imagery can then be used as a basis to determine the pavement surface condition and subsequently support maintenance orders and manage pavement repairs. Further, metadata such as latitude, longitude, and altitude geo-location coordinates and sampling time can also be transferred to the output products to create a digital time history and enable analysis for preventative maintenance planning. Alternatively-sourced imagery may also be analyzed.

A processing method includes obtaining a processing image of a apparatus and performing a second processing on the processing image to generate a target image to analyze the target image according to a target defect detection method to realize defect detection of the apparatus. The processing image is obtained by performing a first processing on an initial image of the apparatus. The first processing includes performing scale processing on the initial image according to defect parameters corresponding to the initial image.

To efficiently search a processing condition of giving a desired target processing result, there is provided a processing condition determination system including a processing apparatus that processes a sample, a processing monitor system that monitors the state of the processing in the processing apparatus, and an analysis system that sets the processing condition of the processing apparatus of giving a target processing result, wherein the system includes a processing condition and result database that stores a set of an explanatory variable that is a processing condition under which the processing apparatus processes a sample and an objective variable that is the processing result obtained by the processing apparatus' processing the sample, and when the processing apparatus processes the sample under the processing condition set using the correlation model derived from the database and it is determined that a probability of failure occurrence becomes high, based on the monitor data of the processing monitor system, the processing apparatus stops the processing under the present processing condition and the analysis system resets a new processing condition.

A deterioration diagnosis device according to an example aspect of the present invention includes: a memory; and at least one processor coupled to the memory. The processor performs operations. The operations include: acquiring an image including a portion to be diagnosed in a structure; calculating, by using the image, deterioration degree that is a degree of deterioration of the portion; calculating reliability for the deterioration degree based on imaging information that is information related to capturing of the image; and outputting the deterioration degree and the reliability in association with each other.

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.

The image inspection apparatus includes an image score calculator and an imaging condition specifier. The image score calculator calculates scores of images, which are produced under different imaging conditions. The imaging condition specifier receives, when two or more thumbnails corresponding to two or more of the images that have a higher score are displayed on the display, selection of one from the two or more thumbnails to specify a set of imaging conditions corresponding to the thumbnail selected. The different imaging conditions include a single-shot set of conditions under which a single-shot image is produced, and a composition series of sets of conditions under which images are captured to produce a composite image from the images. The display shows a reference information display area that indicates reference information representing the single-shot set or composition series of sets in response to the displaying of the two or more thumbnails.

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 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.

To efficiently search a processing condition of giving a desired target processing result, there is provided a processing condition determination system including a processing apparatus that processes a sample, a processing monitor system that monitors the state of the processing in the processing apparatus, and an analysis system that sets the processing condition of the processing apparatus of giving a target processing result, wherein the system includes a processing condition and result database that stores a set of an explanatory variable that is a processing condition under which the processing apparatus processes a sample and an objective variable that is the processing result obtained by the processing apparatus' processing the sample, and when the processing apparatus processes the sample under the processing condition set using the correlation model derived from the database and it is determined that a probability of failure occurrence becomes high, based on the monitor data of the processing monitor system, the processing apparatus stops the processing under the present processing condition and the analysis system resets a new processing condition.

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.