A Multiple Sensors Approach to Wood Defect Detection

In the forest products manufacturing industry, recent price increases in the cost of high-quality lumber together with the reduced availability of this resource have forced manufacturers to utilize lower grade hardwood lumber in their manufacturing operations. This use of low quality lumber means that the labor involved in converting this lumber to usable parts is also increased because it takes more time to remove the additional defects that occur in the lower grade material. Simultaneously, labor costs have gone up and availability of skilled workers capable of getting a high yield of usable parts has markedly decreased. To face this increasingly complex and competitive environment, the industry has a critical need for efficient and cost-effective new processing equipment that can replace human operators who locate and identify defects that need to be removed in lumber and then remove these defects when cutting the lumber into rough parts. This human inspection process is laborious, inconsistent and subjective in nature due to the demands of making decisions very rapidly in a noisy and tiring environment. Hence, an automatic sawing system that could remove defects in lumber while creating maximum yield, offers significant opportunities for increasing profits of this industry. The difficult part in designing an automatic sawing system is creating an automatic inspection system that can detect critical features in wood that affect the quality of the rough parts. Many automatic inspection systems have been proposed and studied for the inspection of wood or wood products. But, most of these systems utilize a single sensing modality, e.g., a single optical sensor or an X-ray imaging system. These systems cannot detect all critical defects in wood.