A system for monitoring the tread of a shoe includes a frame member, a waveguide structured to support the shoe when an outer sole of shoe is in contact with a surface of the waveguide, a light source structured to provide light within an edge of the waveguide such that the light will be internally reflected within the waveguide, and an imaging device structured and configured to generate an image of the outer sole of the shoe when the outer sole of shoe is in contact with the waveguide and the light is being provided within the waveguide. The system also includes a controller structured and configured to: (i) receive image data based on the image of the outer sole; (ii) determine from the image data a largest worn region size for the shoe; and (iii) determine a slip risk for the shoe based on the largest worn region size.

A leather defect detection system comprises a worktable, a conveying mechanism, an image capture module, a model training computing device and an embedded computing device, the worktable is used to place a to-be-detected leather; the conveying mechanism is movably disposed on the worktable; the image capture module is disposed on the conveying mechanism, when the conveying mechanism is actuated, relative positions of the image capture module and the to-be-detected leather change synchronously to capture a plurality of to-be-detected images respectively; the model training computing device uses a plurality of historical leather images captured by the image capture module to perform calculation to establish a defect identification model, and the defect identification model is transcoded into the embedded computing device, so that the embedded computing device is capable of directly using the transcoded defect identification model to perform defect identification on the to-be-detected images.

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.

An approach for collecting disparate data within a pipe involves a sensor arrangement configured to be deployed within the pipe. The sensor arrangement includes a plurality of sensors configured to detect disparate data related to the pipe. Each sensor of the plurality of sensors is coupled to a respective collection computer on the sensor arrangement. A synchronization module is configured to synchronize the disparate data. A database is configured to store the synchronized data. A processor is configured to process the synchronized data. A user interface configured to present the synchronized data to a user.

A method detects mechanical equipment parts. The method includes: obtaining an image of a part; extracting a feature from the image using a machine learning model, identifying a type of surface defect on the basis of the feature to obtain an identification result; and determining whether to replace the part on the basis of the identification result and a predetermined standard of the part. The method reduces the difficulty of detecting a part, can accurately identify a surface defect of the part and determine whether the part needs to be replaced, thereby improving the work efficiency, and shortens the time for mechanical equipment to stop operating for maintenance, thus improving the operating efficiency of the mechanical equipment. The method is automatically executed by a computer, thereby avoiding manually checking errors, improving the accuracy of detection results, and thus improving the reliability of operation of the mechanical equipment.

A processor identifies print defect locations where printing defects occur and a assigns different sensitivity values to different zones of a job image being prepared for printing by a printing engine. The processor changes the orientation of the job image to a revised orientation, relative to print media, to avoid locating one or more high-sensitivity zones of the job image in the print defect locations. The high-sensitivity zones are ones of the zones having a sensitivity value above a threshold sensitivity value. The printing engine prints the job image on the print media at the revised orientation.

Stack fault inspection apparatus and method are disclosed. The apparatus includes a sample stage fixing the silicon carbide substrate and allow the incident light to scan the substrate surface; an incident light source configured to irradiate a vertical illumination light of a wavelength corresponding to an energy greater than a band gap energy of the substrate to at least a portion of a surface of the substrate in a direction substantially perpendicular to the surface of the substrate; a photomultiplier tube (PMT) configured to obtain a photoluminescence mapping image having a wavelength corresponding to the band gap energy of the substrate from the surface of the substrate; and a controller configured to process the mapping image and identify stacking faults.

Systems, methods, and devices are provided for the creation of predictable and accurate defects in a fiber tow of an Automated Fiber Placement (AFP) process, with such artificial defects being useful to support calibration of an in situ inspection system used in the AFP process. Various embodiments include methods for creating such artificial defects that support calibration of an in situ inspection system of an AFP system or process. Various embodiments may also include a defect stencils for an AFP system or process.

A semiconductor-inspection tool scans a semiconductor die using a plurality of optical modes. A plurality of defects on the semiconductor die are identified based on results of the scanning. Respective defects of the plurality of defects correspond to respective pixel sets of the semiconductor-inspection tool. The scanning fails to resolve the respective defects. The results include multi-dimensional data based on pixel intensity for the respective pixel sets, wherein each dimension of the multi-dimensional data corresponds to a distinct mode of the plurality of optical modes. A discriminant function is applied to the results to transform the multi-dimensional data for the respective pixel sets into respective scores. Based at least in part on the respective scores, the respective defects are divided into distinct classes.

A method for inspecting a wafer includes: rotating the wafer about an axis of the wafer, emitting from a light source, two pairs of incident coherent light beams, each pair forming, at the intersection between the two beams, a measurement volume, a portion of the main wafer surface passing through each of the measurement volumes during the rotation, collecting a light beam scattered by the wafer surface, capturing the collected light and emitting an electrical signal representing the variation in the collected light intensity, detecting in the signal, a frequency, being the time signature of a defect through a respective measurement volume, for each detected signature, determining a visibility parameter, on the basis of the visibility determined, obtaining an item of information on the size of the defect, and cross-checking the items of information to determine the size of the defect.