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The Research of Method for Surface Defects Detection of Laminated Flooring Based on Machine Vision

Author: YangLiLi
Tutor: HanNing
School: Beijing Forestry University
Course: Control Theory and Control Engineering
Keywords: Machine Vision Laminate flooring Image Segmentation Feature Extraction BP neural network
CLC: TP274
Type: Master's thesis
Year: 2010
Downloads: 161
Quote: 0
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Abstract


In diameter timber as a raw material commonly known as man-made laminate flooring wood floor , with wear , beautiful, environmentally friendly, moisture-proof , flame retardant , moth , easy to install , easy to clean and care , economical and practical advantages , and has a price advantage, so the application development quickly . However, in the domestic production line of laminate flooring , wood paste for product surface appearance quality decorative paper is still rely on manual visual inspection , labor-intensive , not only likely to cause visual fatigue , and because different understanding of different inspectors , test results were affected by subjective factors produce differences in the appearance of quality is difficult to guarantee . While manual testing is slow and inefficient. Tries to explore the topic of artificial intelligence methods laminate flooring surface quality inspection , improve the level of automation of the production line . This paper discusses in detail the use of video cameras laminate flooring line quality inspection of specific methods , including image processing, feature extraction and pattern recognition , these three steps are the core of machine vision research . The authors first through research Beijing Kenuo Sen Chinese wood flooring factory production enterprise technology requirements, developed a research technology roadmap . Link in image segmentation , the paper tests the ant colony algorithm , Otsu algorithm and genetic algorithm three kinds of maximum entropy approach , the results show that genetic algorithms are better able to extract the light and dark floor surface defects , and the computing speed compared with fast. This paper selected laminate flooring surface defects color and texture features for a parameter calculation, and the use of principal component analysis of these parameters do dimensionality reduction , effectively reducing the complexity of post- op . Finally, we use BP neural network model for the recognition of experimental classification , completed the laminate flooring surface quality intelligent detection method for the entire process. The MATLAB simulation results show that the use of BP neural network laminate flooring surface defect detection is not ideal and can not be used in actual production .

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