Stock management for wood and lumber products requires measuring and counting items individually on a continuous basis; considering a single lumber package alone can contain hundreds of pieces, it is a tedious task that is error prone when done manually. The invention provides a technology solution that involves taking a picture of products using a smart-phone, or a tablet's built-in camera, processing said picture data to detect individual items using Artificial Intelligence Object Detection methods, and utilizing special algorithms to measure and compute unit volume to present the user a detailed description, measure, count, and a summary. This process helps identify and take stock counts faster and with higher accuracy.

A system and method for detecting the origin of wooden planks in a sawmill is provided. The method scans surfaces of processed planks and, with the help of an AI algorithm comprising a deep-learning algorithm, determines the origin of said planks based on analysed parameters of the planks. The parameters used in the analysis are mainly properties of tool marks and the resulting analysis provides tools and equipment used. The deep learning algorithm may be in a self-learning mode or in a supervised training mode.

Computer implemented method for training a software infrastructure based on machine learning techniques and intended for analysis of data obtained from a three-dimensional tomographic inspection of objects of a predetermined type, such as logs, with the aim of determining information about internal characteristics of interest of the self-same objects, wherein, once a training set comprising a plurality of objects of the same predetermined type has been selected, for each object the software infrastructure is supplied with training input data and corresponding training output data, which are processed by the software infrastructure for setting internal processing parameters of the software infrastructure which correlate the training input data with the training output data; where the training input data comprise data obtained from a three-dimensional tomographic inspection of the object, and the training output data comprise information about internal characteristics of interest assessed at internal points of the object, and where the information about the internal characteristics of interest is at least partly assessed at real internal points of the object, previously made accessible by cutting or breaking the object.

A method and a system for patch-based scene segmentation using neural networks are presented. In an embodiment, a method comprises: using one or more computing devices, receiving a digital image comprising test image; using the one or more computing devices, creating, based on the test image, a plurality of grid patches; using the one or more computing devices, receiving a plurality of classifiers that have been trained to identify one or more materials of a plurality of materials; using the one or more computing devices, for each patch of the plurality of grid patches, labelling each pixel of a patch with a label obtained by applying, to the patch, one or more classifiers from the plurality of classifiers; using the one or more computing devices, generating, based on labels assigned to pixels of the plurality of grid patches, a grid of labels for the test image.

A method of board lumber grading is performed in an industrial environment on a machine learning framework configured as an interface to a machine learning-based deep convolutional network that is trained end-to-end, pixels-to-pixels on semantic segmentation. The method uses deep learning techniques that are applied to semantic segmentation to delineate board lumber characteristics, including their sizes and boundaries.

A black and white image of a wood product, such as a veneer sheet, is captured with a first camera and a color image of the wood product is captured with a second camera. Computer processing of the black and white image is performed to determine dimensions of the wood product, the existence of voids within the wood product, and the presence of debris on the wood product. Computer processing of the color image is performed to determine whether colored defects are present in the wood product. A grade is assigned to the wood product based on this computer processing. The wood product can then be sorted based on the grade.

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.

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.

The invention relates to a method for operating a pass-through machine (1), in particular a pass-through machine which is provided for use in the manufacture of furniture and components, and to a pass-through machine. In the method, a supplied workpiece (W) is first detected and/or identified using a workpiece detection device (20); handling information (HI) is then output using an information device (30) on the basis of the detected information of the workpiece detection device (20); whereupon an operator of the pass-through machine (1) carries out a handling process on or with the workpiece (W) according to the output handling information (HI); and the handled workpiece (W) is then supplied to a machining device (50) of the pass-through machine (1) using a supply device (80).

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.