A belt examination system includes a defect candidate detecting processor that detects a candidate for a belt defect that is an abnormal portion of an intermediate transfer belt of an image forming apparatus from a belt image that is an image of the intermediate transfer belt, the defect candidate detecting processor executes a background pattern reduction step to reduce a texture-pattern like background noise present in the belt image and detects the candidate based on the belt image generated during the background pattern reduction step, and the background pattern reduction step is to replace, in the belt image, a color value within a specific range of color values not including a lowest color value of the belt defect with a specific color value within the specific range.

Disclosed herein are systems and methods for synthesizing a diamond using a diamond synthesis machine. A processor receives a plurality of images of a diamond during synthesis within a diamond synthesis machine, each of the plurality of images captured within a time period. The processor executes a diamond state prediction machine learning model using the plurality of images to obtain a predicted data object, the predicted data object indicating a predicted state of the diamond within the diamond synthesis machine at a time subsequent to the time period. The processor detects a predicted defect, a number of defects, defect types, and/or sub-features of such defects and/or other characteristics (e.g., a predicted shape, size, and/or other properties of predicted contours for the diamond and/or pocket holder) of the predicted state of the diamond. The processor adjusts operation of the diamond synthesis machine.

A surface-defect detecting method of optically detecting a surface defect of a steel material, the method including: an irradiation step of irradiating an examination target part with illumination light beams from different directions by using two or more distinguishable light sources; and a detection step of detecting a surface defect in the examination target part based on the degree of overlapping of bright portions extracted from two or more images formed by reflected light beams of the illumination light beams.

Methods and systems for determining metrology-like information for a specimen using an inspection tool are provided. One method includes determining first process information for first feature(s) formed in first area(s) on a specimen from output generated by output acquisition subsystem(s) that include an inspection subsystem. The method also includes determining second process information for second feature(s) formed in second area(s) on the specimen from the output and at least a portion of the first process information. At least a portion of the second process information is a different type of information than the first process information. At least a portion of a design for the second feature(s) is different than a design for the first feature(s), and the first area(s) and the second area(s) are mutually exclusive on the specimen.

A product defect detection method, device and system are disclosed. The product defect detection method comprises: constructing a defect detection framework including a classification network, a locating detection network and a judgment network; training the classification network by using a sample image of a product containing different defect types to obtain a classification network capable of classifying the defect types existing in the sample image; training the locating detection network by using a sample image of a product containing different defect types to obtain a locating detection network capable of locating a position of each type of defect in the sample image; inputting an acquired product image into the defect detection framework, inputting a classification result and a detection result obtained into the judgment network to judge whether the product has a defect, and detecting a defect type and a defect position when the product has a defect.

The present disclosure provides a method for detecting a defect of a film. The method includes obtaining a film image, determining one or more pieces of scratch information corresponding to the film image through processing the film image using a recognition model, the recognition model includes a convolution layer, a regression layer, and a classification layer, determining whether each piece of scratch information in the one or more pieces of scratch information meets a preset condition, each piece of scratch information includes position information, angle information, and size information, in response to a determination that each piece of scratch information meets the preset condition, adding one or more pieces of annotation information to the one or more pieces of scratch information that meets the preset condition, and generating prompt information based on the one or more pieces of annotation information.

A method includes receiving, by a processor, a plurality of images of a test object, the plurality of images including a plurality of surfaces of the test object. The processor receives an image of a barcode on the test object. The processor selects a region of interest in each of the plurality of images of the test object. The region of interest includes the test object having a background removed. For the plurality of regions of interest as selected, the processor compares each region of interest with a corresponding profile image and identifying defects in each region of interest. The corresponding profile image is determined from the image barcode on the test object. The method includes grading, by the processor, a cosmetic appearance of each region of interest based on the identified defects. The method includes storing the grades of the cosmetic appearance for each region of interest.

There is provided a quantitative method for determining a level of a sanding surface preparation of a carbon fiber composite surface, prior to the carbon fiber composite surface undergoing a post-processing operation. The quantitative method includes fabricating a ladder panel of levels of sanding correlating to an amount of sanding of sanding surface preparation standards for a reference carbon fiber composite surface of reference carbon fiber composite structure(s); using surface analysis tools to create target values for quantifying the levels of sanding; measuring, with the surface analysis tools, sanding surface preparation location(s) on the carbon fiber composite surface of a test carbon fiber composite structure, to obtain test result measurement(s); comparing the test result measurement(s) to the levels, to obtain test result level(s); determining if the test result level(s) meet the target values; and determining whether the carbon fiber composite surface is acceptable to proceed with the post-processing operation.

An image restoration apparatus includes: an image acquisition unit that acquires a mask image generated by masking, by a mask region, a predetermined region of an article image in which an article is imaged; and an image output unit that outputs a restored image corresponding to the mask image acquired by the image acquisition unit by using a restorer pre-trained by machine learning so as to output the restored image, which is restored from the mask image to reproduce the article image in a pseudo manner, according to an input of the mask image.

A method of inspecting a component for anomalous regions includes recording a plurality of sensor readings using one or more sensors, each reading corresponding to a different region of the component; determining an appearance metric for each reading; and determining a nominal appearance metric based on individual values of the appearance metric for a first subset of the readings. The method includes, for a particular sensor reading outside the first subset: determining a difference between the nominal appearance metric and the appearance metric of the particular sensor reading; updating the nominal appearance metric based on the particular sensor reading; and, based on the difference exceeding a threshold: determining that the particular sensor reading is anomalous and corresponds to an anomalous region of the component, and recording additional sensor readings of the anomalous region using one or more sensing parameters that differ from those used to record the anomalous sensor reading.