【作者】 杨丽丽；

【导师】 韩宁；

【作者基本信息】 北京林业大学 ， 控制理论与控制工程， 2010， 硕士

【摘要】 以小径材为原材料强化木地板俗称人造木地板,具有耐磨、美观、环保、防潮、阻燃、防蛀、安装便捷、易于清洁护理、经济实用等诸多优点,且具有价格优势,所以应用发展迅速。然而在国内强化木地板生产线上,对于产品表面粘贴的木纹装饰纸的外观质量检验目前还是依靠人工目测,劳动强度大,不仅容易造成视觉疲劳,而且由于不同检测员的认识不同,检测结果受主观因素影响产生差异,外观质量难以保证。同时人工检测速度慢,效率低。本课题试图探索人工智能方法的强化木地板表面质量检测,提高该生产线的自动化水平。本文详细论述了利用视频摄像机进行强化木地板在线质量检测的具体方法,包括图像处理、特征提取和模式识别,这三个步骤是机器视觉研究的核心内容。作者首先通过调研北京科诺森华木地板厂等生产企业的技术要求,制定了课题研究的技术路线。在图像分割环节,本文试验了蚁群算法、最大类间方差算法和最大熵的遗传算法三种处理方法,结果表明遗传算法能够更好地提取地板表面浅色和深色缺陷,且运算速度较快。本文选取强化木地板表面缺陷的颜色特征和纹理特征进行了参数计算,并采用主成分分析法对这些参数做了降维处理,有效减少了后期运算的复杂程度。最后运用BP神经网络对实验样板进行了识别分类,完成了强化木地板表面质量智能检测方法研究的全过程。经MATLAB仿真表明,运用BP神经网络对强化木地板表面瑕疵进行检测效果不够理想,不能应用于实际生产。更多还原

【Abstract】 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.更多还原