An example wear detection system receives a plurality of images from a plurality of sensors associated with a work machine. Individual sensors of the plurality of sensors have respective fields-of-view different from other sensors of the plurality of sensors. The wear detection system identifies a first region of interest and second region of interest associated with the at least one GET. The wear detection system determines a first set of image points and a second set of images points for the at least one GET based on geometric parameters associated with the GET. The wear detection system determines a wear level or loss for the at least one GET based on the GET measurement.

Provided is an image and an inspection system that make it possible to effectively utilize a result of past inspection. An audit position indicating a position where an audit is planned or a position where an audit is completed and a past audit position indicating a position where an audit was performed in the past within a predetermined range including the audit position are displayed over a map on a user terminal, the map being a drawing representing a configuration of a manufacturing site. The inspector references the map on the user terminal and effectively utilizes a past audit result to perform an audit.

A defect inspecting system includes a detector configured to image a sample and a host control device that acquires an inspection image including a defect and a plurality of reference images not including a defect site and generates a pseudo defect image by editing a predetermined reference image among the plurality of acquired reference images. An initial parameter is determined with which the pseudo defect site is detectable from the pseudo defect image. The host control device acquires a defect candidate site from the inspection image using the initial parameter, estimates a high-quality image from an image of a site corresponding to the defect candidate site using the parameter acquired in image quality enhancement, and specifies an actual defect site in the inspection image by executing defect discrimination. A parameter is determined with which a site close to the specified actual defect site is detectable using the inspection image.

Method, imaging system (5), data processing device (60) and system (10) for determination of a 3D information (90), especially of a point cloud (80) or of a 3D surface reconstruction (81) or of a 3D object (82), of an inner part (55) of a metallurgical vessel (50) or of a modification, the method comprising the steps of providing (100) a metallurgical vessel (50); capturing (110) a first optical image (21) of at least one first inner part (51) of the metallurgical vessel (50), from a first imaging device position (22) outside of the metallurgical vessel (50), with a first optical axis (23), by a first imaging device (20); capturing (120) a second optical image (31) of at least one second inner part (52) of the metallurgical vessel (50), from a second imaging device position (32) outside of the metallurgical vessel (50), with a second optical axis (33), by a second imaging device (30); calculating (130) a 3D information (90), such as a point cloud (80) or a 3D surface reconstruction (81) or a 3D object (82), of at least one inner part (55) of the metallurgical vessel (50) from at least the first optical image (21) and the second optical image (31), whereas the first optical image (21) is captured from a first fixed imaging device position (22) with a first fixed optical axis (23) and whereas the second optical image (31) is captured from a second fixed imaging device position (32) with a second fixed optical axis (33).

A device for testing the contents of a switch cabinet following installation of equipment components (2a-2e) according to a plan and the wiring thereof via terminals (6) provided therefor by means of designated electrical cables (5), includes a camera unit (7) for the at least two-dimensional image capture of the components (2a-2e) fully installed and wired on an installation board (4) of a switch cabinet (1). The device can further include an evaluation unit (8) for carrying out a comparison between an imaged layout (100), on the basis of the image capture, of the actual state with a provided plan layout (200) of the target state.

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 printing system includes processing circuitry. The processing circuitry acquires print data of a plurality of pages and extracts comparison data from the print data for each page. The processing circuitry, from first image data read from a printed material on which the print data is printed, acquires second image data at a position corresponding to the comparison data, for each page of the printed material. The processing circuitry outputs a comparison result of the comparison data and the read image data for each page.

An example wear detection system receives first image data related to at least one ground engaging tool (GET) of a work machine from one or more sensors at a first time instance in a dig-dump cycle of the work machine. The wear detection system processes the first image data to determine a first wear measurement and first wear level for the at least one GET. The wear detection system determines whether the first wear level is indicative of a GET replacement condition. The wear detection system generates an alert when the first wear level is indicative of the GET replacement condition. The wear detection system receives second image data related to the at least one GET a second time instance different from the first time instance when the first wear level is not indicative of the GET replacement condition and determines a second wear measurement and second wear level for the at least one GET. The wear detection system generates an alert indicative of the first wear level and the second wear level based on determining that the first wear level and the second wear level are indicative of the GET replacement condition.

Systems and methods for inspecting the outer skin of a honeycomb body are provided. The inspection system comprises a rotational sub-assembly configured to rotate the honeycomb body, a camera sub-assembly configured to image at least a portion of the outer skin of the honeycomb body as it rotates, a three-dimensional (3D) line sensor sub-assembly configured to obtain height information from the outer skin of the honeycomb body; and an edge sensor sub-assembly configured to obtain edge data from the circumferential edges of the honeycomb body. In some examples, the inspection system utilizes a universal coordinate system to synchronize or align the data obtain from each of these sources to prevent redundant or duplicative detection of one or more defects on the outer skin of the honeycomb body.

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.