Abstract: The present invention introduces a saw line flow management method that incorporates deep learning AI, machine vision, 3D data, and robotics to monitor and maintain the smooth operation of a lumber production line in a sawmill or planer mill. By utilizing surveillance cameras and AI models, the system assesses board integrity, quality grading, and line flow anomalies. For a board evaluation, 3D data and AI deep learning models analyze live video feeds to detect major defects or positioning issues. Anomalies in the production line flow are identified using AI models applied to video feeds of the conveyor area. The system can trigger alarms and initiate human or robotic interventions to remove problematic boards or rectify flow disruptions.

Abstract: 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.

Abstract: 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.