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

In accord with the national sustainable developing policies,laminated flooring is growing rapidly and having enormous room for future growth.The surface quality is an integrated-index which directly influences the application of laminated flooring.However,in domestic laminated flooring production line,outward appearance quality inspection still depends on human visual.Thus,this paper brings in the research of method for surface quality detection of laminated flooring which basing on machine vision.The studying subject of this paper is a certain laminated flooring produced by former Beijing Kenuo Senhua floor factory,and mainly research on its most common four defects which are frosting, bare substrate,dirt and tearing of impregnated paper.The method for surface quality detection of laminated flooring basing on machine vision includes the image fetching and segmentation,Feature parameter calculating and extraction,and classifier designing.The mission of segmenting is that the target defects from the complicated wood-grain background should be achieved during the stage of image segmentation.And then calculating based on analyzing the laminated flooring defects to find out the feature parameter which could reflect the qualified and the defected flooring's attribute.At this stage, the parameter need to be dimensions reduced as well,in order to make sure the parameter is distinctive, reliable,independent and modicum,and reduce the complexity of classifier meanwhile.When coming to the classifier designing,we want to create one kind of classifier which can assign different identifiers to the qualified floor and defective floor.This paper did a systemic study for Surface Defects Detection of laminated flooring.The main contents are as follow:1,At the stage of image segmentation,this paper puts forward three image segmentation methods: Image segmentation based on maximum between-cluster variance;Image Segmentation by Ant Colony Algorithm in two-dimension space;Image Segmentation based on Genetic Algorithm and the maximum entropy,and compares their explicabilities on detecting laminated flooring with each other.The segmentation based on OTSU is not applicable for light color defects including frosting;on contrary,the segmentation based on Ant Colony Algorithm is not applicable for dark color defects including bare substrate,dirt and tearing of impregnated paper defects;The method based on Genetic Algorithm has good segmentations for dark and light color defect,so,the method is applicable for surface image segmentation of Laminated flooring.2,In the research of feature extraction,this paper advances that fetching out the color and grain features of images can express the character of floor based on analyzing the sampled laminated flooring surface image.In the calculation of the color parameter,the HIS three-dimensional space is reduced to one-dimensional space by weighting and summing.Then the first,second and third moment of color moment are calculated in the one-dimensional space.The real and imaginary parts of the third moment are conducted as two features.In the calculation of the texture parameter,the five fetures of energy, moment of inertia,entropy,correlation and local stationarity at four angle directions of 0~0,45~0,90~0 and 135~0 should be calculated and averaged,the standard deviation is resulted as well.3,Targeting the problem that the high-dimensional data would increase the complexity degree of algorithm space and time,this paper use PCA linear parameter to reduce the twenty-four-dimensional parameter result,and get a new four-dimensional feature parameter,which effectively solve the recognizing-speed and storage-capacity problems caused by high-dimensional data.4,Paper use RBF and BP neural network structure to progress the classifier design.Then feature parameters of the twenty-four-dimension and the four-dimension are classifier respectively by the two networks.The result shows that the RBF network has more advantage than the BP network in detection of surface defects for laminated flooring after comparing these two networks.