Identification of wood defect using pattern recognition technique

Abstract

Before introducing AVI in the wood industry, a normal inspection process would require human operators to visually inspect the timber to locate and identify the timber defects. The interpretation of the human operator on the timber defects would then determine how the timber defects are categorized, either as permissible or non-permissible. Nonetheless, human operators in the wood industry are usually tasked with the examination job, but due to tiredness and boredom, in the long run, their performance are usually unsatisfactory. A study had shown that three-quarters of the decision made by human operators are inaccurate, which leads to an absolute yield loss of roughly 16.1% from an overall yield of 63.5% to a lower yield of 47.4% [1]. On the contrary, Automated Vision Inspection (AVI) was being highlighted as one of the solutions to ensure the constant reliability of the product and at the same time cater to the current arising issues which resulted in the loss of the yield due to poor accuracy inspection done by human operators. This study attempts to refine one pattern recognition technique suitable for detecting nine defect types from four wood species, namely Rubberwood, Kembang Semangkuk (KSK), Merbau, and Meranti. Besides that, the study also tries to determine the relevance of using a neural network as a classification model in detecting wood defects. Aside from that, the parameters used for a neural network model could be used as a key reference on other standard classifiers. ARTICLE I NFO ABSTRACT