Fundamental study on automatic detection and discrimination of surface defects in wood.

There are many types of defects in wood, some of which reduce strength of structure parts or spoil the appearance of furniture parts. To make efficient use of valuable wood free from such defects, it is desired to develop an automated inspection system which is able to locate the position and extent of each defect present and to identify the type of defect at each location. In this paper, we propose an automatic inspection method to detect and discriminate surface defects in wood. The essential points of the method are as follows : Input gray level image of testing wood is divided into a number of regions and several tonal property measures are extracted from image of each region, and fuzzy clustering technique is adopted for detection and discrimination of surface defects. Applying the proposed method to inspection tests of actual lumbers, we obtained the following results : (1) Defect detection can be achieved almost completely. (2) Discrimination rate ranges from 25 to 82% depending on the type of defect, and this indicates that more research is needed.