The invention relates to a method for producing an OSB, wherein a scattered mat formed by strands adhered in multiple layers is pressed in a hot press to form a panel with a desired thickness, comprising the following steps: a) scanning the surface of an upper side of the mat or of the pressed panel to determine uneven areas and/or faults; b) determining position data of the determined uneven areas and/or faults; c) determining the volumes of the individual uneven areas and/or faults; d) targeted filling of the uneven areas and/or faults with a filling material, based on the determined position data and volumes, wherein e) the filling material is scattered with a scattering device.

A method of assessing damage to a component includes displaying a sensor image of the component in a first viewing pane, displaying a reference image of the component, which is a graphical depiction of the component with accurate dimensions, in a second viewing pane, placing a plurality of first identification markers on the sensor image of the component in the first viewing pane to correspond to a matching location with a second identification marker on the component in the reference image, identifying a region of damage on the component in the sensor image, mapping the region of damage to the component in the reference image using the plurality of first and second identification markers, and calculating a size of the region of damage.

A method for remote visual inspection of a target surface of a structure, comprising positioning an inspection unit at a fixed location, the inspection unit comprising a combination of dynamic digital video cameras and an optic supported by a pitch axis of a multi-axis assembly comprising three moveable axis, including a vertical axis, a roll axis connected to the vertical axis, and the pitch axis, the pitch axis being connected to the roll axis; and a controller connected to the combination and the multi-axis assembly; adjusting the positioned inspection relative to the target surface; calibrating a target surface of the structure; taking images of the calibrated surface of the structure, each image including at least position data of the target surface and angular data of the inspection unit; and detecting, in the images, defects on the target surface, and, from the position data and angular data, positioning the defects on the target surface and determining the dimensions of the defects.

The invention includes a pulse oscillated light source, an illumination unit that guides light output from the light source to a sample, a scanning unit that controls a position at which the sample is scanned by the illumination unit, alight converging unit that converges light reflected from the sample, a first photoelectric conversion unit that outputs an electric signal corresponding to the light converged by the light converging unit, an AD conversion unit that converts the electric signal output from the first photoelectric conversion unit into a digital signal in synchronization with pulse oscillation of the light source, a linear restoration unit that processes a digital signal converted by the AD conversion unit in synchronization with a pulse oscillation output by the AD conversion unit and corrects nonlinearity of the first photoelectric conversion unit, a defect detection unit that detects a defect of the sample based on an output of the linear restoration unit, and a processing unit that obtains and outputs a position and a size of the defect detected by the defect detection unit.

A system and method for inspection and cosmetic grading of objects is provided. Camera and lighting assemblies capture images of an object and create a 2D composite image which is processed by an image processing module with a deep learning machine algorithm to detect surface defects in the object. Detected defects are localized and measured for depth of defect by an advanced optical sensor, providing a 3D representation of defects. A cosmetic grading algorithm determines the cosmetic grade of the object and the optimal path of disposition for the item based on the grade.

A flaw inspection apparatus according to the present invention includes a deep learning unit to which an image obtained by photographing a surface of an inspection object is input and in which, on the basis of the input image, the deep learning unit judges absence or presence of a flaw on a surface of the inspection object and specifies a site judged as being the flaw; a dimension measuring unit that measures a dimension of the flaw on the basis of the image of the site specified by the deep learning unit; and a flaw classifying unit that classifies the flaw on the basis of the dimension of the flaw measured by the dimension measuring unit.

An item inspection apparatus includes an image obtaining section and a control section. The image obtaining section obtains a captured image of at least a part of an item. The control section determines whether the item is defective by using the captured image of the item. The control section performs a first inspection for at least one of position, shape and size with respect to at least one of a first element and a second element. The first element has a predetermined shape and is formed on the item and the second element includes at least one of an opening, a depression and a through-hole formed in the item. The control section performs a second inspection for at least one of foreign substance adhesion, scratch and surface stain on the item. Thus, the defect inspection may be performed more precisely and effectively.

A defect detecting device includes an illumination that irradiates a measuring object with illumination light, an imager that images the illumination light reflected by the measuring object, and a detector that detects a defect at a surface of the measuring object based on a captured image obtained by imaging the illumination light by the imager. The captured image includes a plurality of spectral images having different spectral wavelengths, and the detector detects a diffuse reflection region by which the illumination light is diffusely reflected based on the plurality of spectral images, and determines a size of the defect based on the spectral wavelength of the spectral image in which the diffuse reflection region is detected.

According to an embodiment, an inspection apparatus includes an irradiation mechanism, an imaging device, a movable mechanism, and a control processor. The irradiation mechanism irradiates an inspection target with light. The imaging device captures an image of the inspection target through a lens. The movable mechanism changes, with respect to an axis extending in an inspection direction for the inspection target, an angle between the lens and a horizontal plane or an angle between the imaging device and the horizontal plane such that a sample surface of the inspection target, a principal face of the lens, and an imaging face of the imaging device conform to the Scheimpflug principle. The control processor adjusts sensitivities in an image of the sample surface captured by the imaging device at different levels in the image depending on a position in a perpendicular direction to the inspection direction.

An inspection apparatus includes a tone correction unit, a dimensional error acquisition unit, and a map generating unit. The correction unit acquires a transmissivity distribution for transmission of light from a light source through an incident surface of an inspection target based on the optical image data to correct a tone of the optical image data so as to eliminate variations in contrast of the optical image data which correspond to the transmissivity distribution. The acquisition unit determines a dimension of the pattern based on the corrected optical image data to acquire a dimensional error that is a difference between the dimension of the pattern and a design value for the pattern. The generating unit generates a map in which the dimensional error is associated with the position coordinates of the table on the inspection target based on the position coordinates and the dimensional error.