Research On Wood Surface Defect Detection Based On Computer Vision

Saw-timber is most needed in wood application, and the surface of saw-timber is one of the important factors to assess wood quality. With large-scaled mechanization and automation of wood processing, people begin to attach more and more importance to manufacturing quality, especially surface defect. Therefore, surface defect detection is becoming more and more important.Based on the theory of computer vision, a research on defect detection of the wood surface is made in paper. Image preprocess, feature extraction and pattem recognition of wood surface defect are also studied by means of digital image processing techniques and neural networks principles of pattern recognition technology. An arithmetic of image disposal was compiled for disfigurement detection to orientate and recognize in the environment of VC++.Image preprocess is the first step for detection, which is vital to the correct extraction of the defection feature. The grey-historam is analyzed through three methods in paper: (1) 256 grade grey-historam statistics for every pixel. (2) 256 grades grey-historam statistics for every 4×4 block pixels. (3) 16 grades grey-historarn statistics for every 4×4 block pixels.Extraction of characteristic quantity will directly affect the distinguishing ratio of wood disfigurement detection system. The paper firstly judged whether there existed defects in the pictures according to the color break in grey-historam. Defect pictures have double peaks in grey-historarn. Generally speaking, the subordinate peak is just the defect part. But it is not absolute. If there is only one peak in the grey-historan, it means that the picture is normal. Experiments show when the value of subordinate peak is 1/10 greater than host peak value, subordinate peak indicates defect color. It would realize the first step for wood detection. and the images are divided into two types: defect images and normal images.Because there is superior tolerance and no limit to data type and distributing function in pattern recognition of neural network, it has a good prospect in application so as to adapt to the complexity in wood surface defect. Using three character values including gray average value, gray variance and defect shape as input value, ten defect types as output. This paper posed BP neural network system model. The designed network system is tested by chosing four type of defects, and LMS arithmetic to train BP neural network. The result shows the average ratio of recognition is 97 percent. The system is viable and available.