Over the last few years, the use of medium density fibreboard (MDF), oriented strand board (OSB) and other particleboard (grade B wood) has increased dramatically in the timber industry. This represents a major challenge for the wood recycling industry, which grinds this wood into chips to sell it to the wood chip industry. For this reason, the demand for an accurate, reliable, cheap and fast system that separates MDF, OSB and other particleboard material from quality wood in a mixed wood waste stream is huge, as this is still performed manually in many places. This paper presents a vision-based solution for such a wood waste sorting system. A main contribution of our work is an industry-ready, performance integration of state-of-the-art computer vision and machine learning techniques into a robust wood waste sorting setup. This system has been implemented in a Belgian company, where it runs 16 hours/day and processes 7.5 tons of wood waste per hour (reaching a Technology Readiness Level of 9). The return on investment was less than one year. From an input stream with 45% – 55% of grade B wood, two output streams are generated, one which contains less than 2% quality wood and one which contains less than 5% grade B wood.
['Maarten Verheyen', 'Wim Beckers', 'Eric Claesen', 'Geert Moonen', 'Eric Demeester']
Vision-based sorting of medium density fibreboard and grade A wood waste