A device for analyzing a grain sample including a light source, an image sensor, and a controller. The light source is configured for illuminating the grain sample. The image sensor is configured for capturing images of the grain sample. The controller is coupled to the image sensor and is configured for receiving the images of the grain sample therefrom and for analyzing the images to detect at least one material other than grain in the grain sample. The light source is configured for illuminating the grain sample with a local light spot having a size that is smaller than a width of an average wheat kernel. The image analysis and the detection of material other than grain may, at least partly, be performed using trained neural networks and other artificial intelligence algorithms.

Provided is a system and method for visual inspection. The system may be used in a quality assurance station at a manufacturing facility or site. The system may evaluate and determine the quality of manufactured or fabricated articles. The system may include a mechanical subsystem for capturing images of the article. The system may include a sensor such as a camera for capturing data, such as images. The system may include an artificial intelligence system to determine if the article suffers from an impermissible defect.

A method and an apparatus for detecting a defect based on a phased pass/fail determination are disclosed. According to at least one aspect of the present disclosure, a method comprising: a process of acquiring a product image which is an image of the product; a first determination process of inputting the product image into a first determination model to perform a pass/fail determination for the product; and a second determination process of inputting the product image into a second determination model to perform a pass/fail determination for the product when the product is determined to be undeterminable as a result of the pass/fail determination of the first determination process.

Systems and methods for acquiring and inspecting lens images of ophthalmic lenses using one or more cameras to acquire the images of the lenses in a dry state or a wet state. The images are preprocessed and then inputted into an artificial intelligence network, such as a convolutional neural network (CNN), to analyze and characterize for type of lens defects. The artificial intelligence network identifies defect regions on the images and output defect categories or classifications for each of the images based in part on the defect regions.

A product defect detection method and apparatus, an electronic device, and a storage medium are provided. A method includes: acquiring a multi-channel image of a target product; inputting the multi-channel image to a defect detection model, wherein the defect detection model includes a plurality of convolutional branches, a merging module and a convolutional headbranch; performing feature extraction on each channel in the multi-channel image by using the plurality of convolutional branches, to obtain a plurality of first characteristic information; merging the plurality of first characteristic information by using the merging module, to obtain second characteristic information; performing feature extraction on the second characteristic information by using the convolutional headbranch, to obtain third characteristic information to be output by the defect detection model; and determining defect information of the target product based on the third characteristic information.

An inspection device includes: a first acquisition unit for acquiring a first defect probability calculated using a first learning model from data regarding an inspection target processed in a first manufacturing process; a second acquisition unit for acquiring a second defect probability calculated using a second learning model from data regarding the inspection target processed in a second manufacturing process after the first manufacturing process; and a determination unit for determining a defect in the inspection target if the acquired second defect probability is greater than or equal to a second threshold value, the determination unit being configured to change at least one of the second learning model and the second threshold value into a condition for enhancing inspection accuracy if the acquired first defect probability is greater than a predetermined first threshold, compared to if the first defect probability is less than or equal to the first threshold value.

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.

Methods and systems for measuring semiconductor structures based on a trained scanning conditional measurement model are described herein. A scanning conditional model is trained based on Design Of Experiments (DOE) measurement data associated with known values of one or more parameters of interest and a set of perturbed values of the one or more parameters of interest. The trained conditional model minimizes the output of an error function characterizing the error between the known values of the perturbed values of the one or more parameters of interest for the given DOE measurement data. During inference, an error value associated with each candidate value of one or more parameters of interest is determined by the trained scanning conditional measurement model. The estimated value of the parameter of interest is the candidate value of the parameter of interest associated with the minimum error value.

A learning processing device that is based on a neural network model and image data obtained by capturing an image of the object to be inspected, and constructs the neural network model used for inspecting the object to be inspected. The learning processing device is provided with a learning unit which performs a learning process under a prescribed learning condition on the basis of a list of the image data including a plurality of learning images and constructs the neutral network model. The learning unit embeds unique model identification data in the neural network model, whenever the neural network model is constructed.

A surface inspection apparatus includes an imaging device that images a surface of an object to be inspected, and a processor configured to: calculate an evaluation value of a texture of the object through processing of an image imaged by the imaging device; and detect reflection of a cause of erroneous calculation of the image within a specific range based on at least brightness information of the image.