An inspection apparatus according to one or more embodiments extracts an attention area from a target image using a first estimation model, performs a computational process with a second estimation model using the extracted attention area, and determines whether a target product has a defect based on a computational result from the second estimation model. The first estimation model is generated based on multiple first training images of defect-free products in a target environment. The second estimation model is generated based on multiple second training images of defects. The computational process with the second estimation model includes generating multiple feature maps with different dimensions by projecting the target image into different spaces with lower dimensions. The extracted attention area is integrated into at least one of the multiple feature maps in the computational process with the second estimation model.

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.

Various embodiments include reflective-mode Fourier ptychographic microscope (RFPM) apparatuses and methods for using the RFPM. In one example, the RFPM includes a multiple-component light source configured to direct radiation to a surface. The multiple-component light source has a number of individual-light sources, each of which is configured to be activated individually. The RFPM further includes collection optics to receive radiation reflected and scattered or otherwise redirected from the surface, and a sensor element to convert received light-energy from the collection optics into an electrical-signal output. Other apparatuses, designs, and methods are disclosed.

A system includes a processor receiving spectrometer data representative of a scanned sample and generated by a spectrometer and a cloud server including a server processor. The server processor receives the spectrometer data generated by the spectrometer from the processor, analyzes the spectrometer data, identifies, based on a machine learning application, one or more unique characteristics of the spectrometer data which uniquely identifies the scanned sample and provides to the processor data representative of a graphical display, which includes an indication of whether or not the scanned sample includes the one or more unique characteristics of the spectrometer data.

A monitoring system and method are presented for use in monitoring a target. The monitoring system comprises: an input utility for receiving input data comprising measured data indicative of optical response of the target measured under predetermined conditions and comprising phase data indicative of a two-dimensional profile of full phase of the optical response of the target in a predetermined two-dimensional parametric space including a two-dimensional range in which said target exhibits phase singularity; an analyzer module for processing said measured data and extracting at least one phase singularity signature of the target characterizing the target status, the phase singularity signature being formed by a number N of phase singularity points, each corresponding to a condition that the physical phase continuously accumulates a nonzero integer multiple m of 2π around said point.

The presently-disclosed technology enables real-time inspection of a multitude of subcomponents of a component in parallel. For example, the component may be a semiconductor package, and the subcomponents may include through-silicon vias. One embodiment relates to a method for inspecting multiple subcomponents of a component for defects, the method comprising, for each subcomponent undergoing defect detection: extracting a subcomponent image from image data of the component; computing a transformed feature vector from the subcomponent image; computing pairwise distances from the transformed feature vector to each transformed feature vector in a training set; determining a proximity metric using said pairwise distances; and comparing the proximity metric against a proximity threshold to detect a defect in the subcomponent. Another embodiment relates to a product manufactured using a disclosed method of inspecting multiple subcomponents of a component for defects. Other embodiments, aspects and features are also disclosed.

Various surface and structural defects are currently inspected visually. This method is labor intensive, requiring large maintenance man hours, and is prone to errors. To streamline this process, herein is described an automated inspection system and apparatus based on several optical technologies that drastically reduces inspection time, provides accurate detection of defects, and provides a digital map of the location of defects. The technology uses a sensor that includes a pattern projection generator for generating a pattern image on the structural surface and a camera for detecting the pattern image generated by the pattern projection generator on the structural surface. Furthermore, the technology utilizes an image processing and correction apparatus for performing a pattern image and structural surface defect map correction and generate a distortion corrected defect map for a surface scan area on the structure that is incident on the sensor.

In some aspects, the additive manufacturing system may access, by a processor of an additive manufacturing system, a machine learning model that is trained to identify defects within a build plane. Also, the additive manufacturing system may capture, by an imaging system of the additive manufacturing system, an image of a build plane of the additive manufacturing system. The build plane can contain an object being manufactured through an additive manufacturing process. In addition, the additive manufacturing system may provide, by the processor, the captured image as an input to the machine learning model. Moreover, the additive manufacturing system may receive, by the processor, an output from the machine learning model identifying a defect in the build plane.

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 of defect discovery can include providing a nuisance bin in a nuisance filter, partitioning the defect population into a defect population partition, segmenting the defect population partition into a defect population segment, selecting from the defect population segment a selected set of defects, computing one or more statistics of the signal attributes of the defects in the defect population segment, replicating the selected set of defects to yield generated defects, shifting the generated defects outside of the defect population segment, creating a training set, and training a binary classifier. This method can be operated on a system. The method can enable a semiconductor manufacturer to determine more accurately the presence of defects that would otherwise have gone unnoticed.