There is provided a method that includes directing one or more infrared cameras at a compaction roller of a composite laying head of a composite layup machine. The one or more infrared cameras are mounted aft of the compaction roller. The method includes applying heat to a substrate by a heater. The heater is mounted forward of the compaction roller. The method further includes using the one or more infrared cameras, to obtain one or more infrared images of the compaction roller, during laying down of one or more composite tows of a composite layup onto the substrate by the compaction roller. The method further includes identifying, based on the one or more infrared images, one or more temperature profiles of the compaction roller, and analyzing identified temperature profiles, to determine one or more of, a layup quality of the composite layup, and a heat history of the composite layup.

The present invention relates to the technical field of identification on adulterated meat, and in particular, to a method for identifying raw meat and high-quality fake meat based on a gradual linear array change of a component. The present invention spatially characterizes changing rules of featured components in the meat with the utilization of sensitivities of the visible/near-infrared spectral signals to changes of the components in the meat and the advantage that spectral scanning can acquire optical signals of the samples spatially and consecutively, further constructs the identification model according to differences in components and spectra of a region of interest in the hyperspectral image by taking a derivative for characterizing rates of change of the featured components.

A material handling system sorts materials utilizing a vision system of multiple vision devices configured with single board computers that each implement an artificial intelligence system in order to identify or classify materials, which are then sorted into separate groups based on such an identification or classification by sorting devices that are each coupled to one of the vision devices.

A method includes patterning a hard mask over a target layer, capturing a low resolution image of the hard mask, and enhancing the low resolution image of the hard mask with a first machine learning model to produce an enhanced image of the hard mask. The method further includes analyzing the enhanced image of the hard mask with a second machine learning model to determine whether the target layer has defects.

A method for quality inspection of laser material processing includes performing laser material processing on a workpiece and generating, by a sensor, raw image data of secondary emissions during the laser material processing of the workpiece. The method also includes determining a quality of the laser material processing by analyzing the raw image data of the secondary emissions.

The present invention relates to a method for analysing a set of food particles, comprising the steps of acquiring a digital colour image of a plurality of particles arranged on a substrate, detecting portions of the image corresponding to each particle, determining the colour of each portion of the determined image, and measuring a distance between the determined colour of one portion of the image and a predetermined colour of a predetermined particle type.

Systems and methods for generating assessments based on construction site images are provided. For example, image data captured from a construction site using at least one image sensor may be obtained. Further, at least one electronic record associated with the construction site may be obtained. The image data and the at least one electronic record may be analyzed to generate at least one assessment related to the construction site. For example, the image data may be analyzed to identify at least one discrepancy between the at least one electronic record and the construction site, and the identified at least one discrepancy may be used in the generation of the at least one assessment.

An object of the present subject matter is to appropriately acquire and analyze a camera image for monitoring and to constantly monitor a monitoring target with high accuracy. A programmable logic controller (PLC) includes: an image processing section for sequentially acquiring the image data of the camera image from the camera sensor and generating characteristic amount data indicating a characteristic amount of image data in a preset monitoring region; a time-series data acquisition section for sequentially collecting characteristic amount data from the image processing section and acquiring time-series data of a characteristic amount; and a monitoring section for monitoring time-series data of a current characteristic amount acquired by the time-series data acquisition section in accordance with a monitoring timing defined by the device of the device memory.

An apparatus for optical inspection of sanitaryware includes: a supporting device configured to receive a piece of sanitaryware to be inspected; at least one camera, configured to capture a plurality of images of the piece of sanitaryware under inspection; an automatic arm, movable between a plurality of operating positions relative to the supporting device, wherein the at least one camera is mounted on the automatic arm; a control unit, configured to receive the plurality of images, wherein the control unit is configured to process the plurality of images as a function of reference data to provide diagnostic information regarding the defectiveness of the piece of sanitaryware.

System and method that includes mapping temperature values from a two dimensional (2D) thermal image of a component to a three dimensional (3D) drawing model of the component to generate a 3D thermal model of the component; mapping temperature values from the 3D thermal model to a 2D virtual thermal image corresponding to a virtual thermal camera perspective; and predicting an attribute for the component by applying a prediction function to the 2D virtual thermal image.