The Research Of Method For Surface Defects Detection Of Laminated Flooring Based On Machine Vision

Laminated flooring derived from smaller diameter of raw materials is also called artificial plank.It has many advantages, like wear-resisting, artistic, economical, practical, and resistant to damages by moisture, flame and moths, as well as convenient-installing.easy-nursing and environmental protecting, in addition, it has the advantage of lower cost.These characteristics make laminated flooring developing rapidly in China.However,in domestic laminated flooring production line,outward appearance quality inspection still depending on human visual. Due to the great labor intensity, it's easy to cause visual fatigue. What's worse, because of the various understanding of different testing personnel, it's difficult to unify the testing standards and guarantee the appearance quality.Thus, the test results are greatly affected by subjective factors, and the efficiency of artificial detection is low. This paper aims at using machine vision instead of artificial detection to improve the level of laminated floor producing automation line.In this paper, the method of machine vision for on-line detection of laminated flooring is discussed, including image segmentation, feature extraction and classifier design. These three parts are the core contents of study. At the early stage of the research study, through visiting the factories of Kenuo Senhua floor factory of Beijing, we got a clear understanding of laminated flooring production, including the whole manufacture line. In the image segmentation, this paper puts forward three image segmentation methods: Image Segmentation by Ant Colony Algorithm; Image segmentation based on maximum between-cluster variance; Image Segmentation based on Genetic Algorithm and the maximum entropy. Through the comparison of the three methods, genetic algorithm is better and faster in segmenting floor surface defects. So it has used genetic algorithm for research in this paper. Research in laminated flooring surface defect feature, the characteristics of color and texture feature parameters are calculated, and the principal component analysis is used to reduce the complexity of the classifier operation.At last, it uses BP network structure to recognize and classfy the floor surface quality. By matlab simulation, it shows that using BP network to detect the laminated flooring defects is insufficient, so it cannot applied to practical production.