Irregularities on the surfaces of veneer, such as full veneer sheets and/or veneer core material are detected using Near InfraRed (NIR) technology, including Near InfraRed/Short Wave InfraRed (NIR/SWIR) cameras and detectors. A grade is then assigned to the veneer based, at least in part, on the detected irregularities. The graded veneer is then stacked based, at least in part, on the grade assigned to the veneer. The graded veneer stacks are then provided to local robotic panel assembly and pressing systems that include one or more local robotic panel assembly cells for processing the veneer into layered wood product panels.

Irregularities on the surfaces of veneer, such as full veneer sheets and/or veneer core material are detected using Near InfraRed (NIR) technology, including Near InfraRed/Short Wave InfraRed (NIR/SWIR) cameras and detectors. A grade is then assigned to the veneer based, at least in part, on the detected irregularities. The graded veneer is then stacked based, at least in part, on the grade assigned to the veneer. The graded veneer stacks are then provided to local robotic panel assembly and pressing systems that include one or more local robotic panel assembly cells for processing the veneer into layered wood product panels.

The present disclosure describes methods and systems for virtually calibrating geometric sensors with overlapping fields of view. In some embodiments, a geometric sensor may be virtually calibrated by applying a correction value to profile data obtained by the geometric sensor to generate adjusted profile data. The correction factor may be determined based at least in part on X-Y offsets and/or rotational offsets of prior profile data obtained by the geometric sensor relative to corresponding profile data obtained by a reference geometric sensor, and may be recalculated or updated as new sets of profile data are obtained. The adjusted profile data may be used in place of the original profile data in various data processing operations to functionally offset a positional error of the geometric sensor.

A method and system for production of layered wood products employs local and independently operating robotic panel assembly cells including a vison system for inspecting and grading veneer sheets, one or more veneer handling robots, one or more core handling robots, and one or more glue application robots to produce stacks of layered wood product panels locally near the pressing stations. Consequently, the stacks of layered wood product panels are independently built at, or near, the location of the pressing stations using inspected and graded veneer sheets. This eliminates the need for traditional panel conveyors, traditional layered wood product panel assembly layup lines, and stack press delivery lines. This, in turn, eliminates thousands of moving parts and dozens of people from the layered wood product production process.

A computer implemented method is disclosed herein for monitoring and determining a quality level of incoming raw material from one or more sources. The method includes (1) receiving visual data associated with the incoming raw material; (2) determining an indication of quality level associated with the incoming raw material; and (3) transmitting, to at least one of a graphical user interface (GUI) and a computer log, the indication of quality level and at least one timestamp associated with the visual data. The visual data may include a plurality of images received from one or more cameras configured for monitoring the incoming raw material. A related system is also disclosed herein.

A system for directing a vehicle using detected and tracked pallet pocket comprises the vehicle, a navigation system, and a command system which detect and track a pallet pocket during automated material handling using the vehicle where load positions vary and are not accurately known beforehand.

There is described a method of labeling wood products moved across a handling area of a production line. The method generally has, using a camera, generating an image representing a wood product moving across the handling area at a first moment in time; using a controller, determining coordinates of the wood product at the first moment in time based on the image; and anticipating coordinates of the wood product at a second moment in time assuming an incremental movement of the wood product at a given speed from the determined coordinates of the wood product at the first moment in time; and using a light projector, projecting, at the second moment in time, a wood product label at the anticipated coordinates of the wood product at the second moment in time.

Embodiments of the present disclosure relate to a method and apparatus for outputting information. The method can include: acquiring an image of a to-be-inspected object; segmenting the image into at least one subimage; for a subimage in the at least one subimage, inputting the subimage into a pre-trained defect classification model to obtain a defect category corresponding to the subimage; and outputting defect information of the object based on a defect category corresponding to each subimage.

A computer-based system (e.g., a handheld device) is configured to detect, count, and measure logs in a stack. A user captures one or more images of the stack. Where multiple images are captured, the system creates a working image by stitching together portions of the image. The system identifies a contour indicating the outline of the stack in the working image and fits ellipses to the log faces in the working image. Information such as the number of logs, the volume of wood in the stack, and the average log diameter may be made available for presentation to the user.

A method and system for a veneer grading and stacking uses one or more vision systems to generate images of the individual full or partial sheets of veneer and accurately determine the dimensions of each individual full or partial sheet of veneer. The one or more vision systems are also used to analyze the surface of each individual full or partial sheet of veneer quickly and automatically and assign a grade to each individual full or partial sheet of veneer. One or more veneer selection and stacking robots are then used to move each individual full or partial sheet of veneer from a veneer analysis and selection conveyor system to an appropriate veneer stack based on the grade assigned to the individual full or partial sheet by the one or more vision systems.