彩色带花纹瓷砖缺陷检测的算法研究
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摘要
针对彩色带花纹瓷砖色差缺陷人工检测效率低,而且稳定性差的问题,提出一种对彩色带花纹的瓷砖色差检测算法。文中的研究对象是静止摆放的彩色带花纹瓷砖,对其进行了多次实验。首先对采集到的瓷砖图像进行色彩空间转换,对转换后图像的V分量进行中值滤波处理,接着对滤波后的V分量图像进行二值化处理,然后对处理后的瓷砖样本图像进行灰度直方图分割,选择最合适的阈值将彩色带花纹瓷砖从背景图像里分割出来,最后再对分割出来的瓷砖进行分析。经过多次实验可知,该方法对处理彩色带花纹瓷砖色差缺陷效果显著。
In order to solve the problems of low efficiency and poor stability for detecting the chromatic aberration defects on the color patterned ceramic tile,a color difference detection algorithm for the color patterned ceramic tiles is proposed. The research object of this paper is the statically-placed color patterned ceramic tiles,and the related experiments for the ceramic tiles have been carried out. In the algorithm,the color space conversion is performed for the acquired ceramic tile image,the median filtering processing is conducted for the V component of the converted image,the binarization processing is accomplished for the filtered V component image,and then the gray histogram segmentation of the processed tile sample image is performed to select the most suitable threshold value to segment the color patterned ceramic tile from the background image. The segmented tile is analyzed. The conclusion got in the experiments shows this method has a remarkable effect on the processing for the color difference defects of the colored ceramic tiles with patterns.
In order to solve the problems of low efficiency and poor stability for detecting the chromatic aberration defects on the color patterned ceramic tile,a color difference detection algorithm for the color patterned ceramic tiles is proposed. The research object of this paper is the statically-placed color patterned ceramic tiles,and the related experiments for the ceramic tiles have been carried out. In the algorithm,the color space conversion is performed for the acquired ceramic tile image,the median filtering processing is conducted for the V component of the converted image,the binarization processing is accomplished for the filtered V component image,and then the gray histogram segmentation of the processed tile sample image is performed to select the most suitable threshold value to segment the color patterned ceramic tile from the background image. The segmented tile is analyzed. The conclusion got in the experiments shows this method has a remarkable effect on the processing for the color difference defects of the colored ceramic tiles with patterns.
引文
[1]晁云,曹利钢.基于机器视觉的陶瓷砖表面缺陷快速检测方法的研究[J].制造业自动化,2013(17):18-20.CHAO Yun,CAO Ligang. Fast detection research of surface defect of ceramic tiles based on machine vision[J]. Manufacturing automation,2013(17):18-20.
[2]王锋,周仁魁,杨小许,等.CCD摄像机图像中心两种标定方法的应用研究[J].光子学报,2006,35(2):294-298.WANG Feng,ZHOU Renkui,YANG Xiaoxu,et al. Applications of two types of calibration method to ccd camera′s image center measurement[J]. Acta photonica sinica,2006,35(2):294-298.
[3] PENG T,YONGHUI H E. Adaptive illumination light source for online machine vision inspection of tin steel strips[J].Baosteel technical research,2013,7(4):25-28.
[4]刘思峰,谢乃明.灰色系统理论及其应用[M].6版.北京:科学出版社,2013.LIU Sifeng,XIE Naiming. Grey system theory and its application[M]. 6th ed. Beijing:Science Press,2013.
[5]应玉龙,项明.局部相位量化特征的织物瑕疵检测算法[J].西安工程大学学报,2015,29(5):541-545.YING Yulong,XIANG Ming. Fabric defect detection algorithm based on local phase quantization[J]. Journal of Xi’an Polytechnic University,2015,29(5):541-545.
[6]胡坤.工件表面划痕和竖条纹缺陷检测算法研究[D].长沙:中南大学,2013.HU Kun. Algorithm research on workpiece surface scratch and stripe defect inspection[D]. Changsha:Central South University,2013.
[7] PARKER J R.图像处理与计算机视觉算法及应用[M].2版.北京:清华大学出版社,2012.PARKER J R. Algorithms for image processing and computer vision[M]. 2nd ed. Beijing:Tsinghua University Press,2012.
[8] KARIMI M H,ASEMANI D.Surface defect detection in tiling Industries using digital image processing methods:Analysis and evaluation[J].ISA transactions,2014,53(3):8-34.
[9]戴卫军.基于机器视觉的蜂窝陶瓷侧面缺陷检测算法研究[J].陶瓷学报,2014(3):291-295.DAI Weijun. Side defects detection algorithm for honeycomb ceramics based on machine vision[J]. Journal of ceramics,2014(3):291-295.
[10] OBA R,POSSAMAI T S,NICOLAU V P. Thermal analysis of a tunnel kiln used to produce roof tiles[J]. Applied thermal engineering,2014,63(1):59-65.
[2]王锋,周仁魁,杨小许,等.CCD摄像机图像中心两种标定方法的应用研究[J].光子学报,2006,35(2):294-298.WANG Feng,ZHOU Renkui,YANG Xiaoxu,et al. Applications of two types of calibration method to ccd camera′s image center measurement[J]. Acta photonica sinica,2006,35(2):294-298.
[3] PENG T,YONGHUI H E. Adaptive illumination light source for online machine vision inspection of tin steel strips[J].Baosteel technical research,2013,7(4):25-28.
[4]刘思峰,谢乃明.灰色系统理论及其应用[M].6版.北京:科学出版社,2013.LIU Sifeng,XIE Naiming. Grey system theory and its application[M]. 6th ed. Beijing:Science Press,2013.
[5]应玉龙,项明.局部相位量化特征的织物瑕疵检测算法[J].西安工程大学学报,2015,29(5):541-545.YING Yulong,XIANG Ming. Fabric defect detection algorithm based on local phase quantization[J]. Journal of Xi’an Polytechnic University,2015,29(5):541-545.
[6]胡坤.工件表面划痕和竖条纹缺陷检测算法研究[D].长沙:中南大学,2013.HU Kun. Algorithm research on workpiece surface scratch and stripe defect inspection[D]. Changsha:Central South University,2013.
[7] PARKER J R.图像处理与计算机视觉算法及应用[M].2版.北京:清华大学出版社,2012.PARKER J R. Algorithms for image processing and computer vision[M]. 2nd ed. Beijing:Tsinghua University Press,2012.
[8] KARIMI M H,ASEMANI D.Surface defect detection in tiling Industries using digital image processing methods:Analysis and evaluation[J].ISA transactions,2014,53(3):8-34.
[9]戴卫军.基于机器视觉的蜂窝陶瓷侧面缺陷检测算法研究[J].陶瓷学报,2014(3):291-295.DAI Weijun. Side defects detection algorithm for honeycomb ceramics based on machine vision[J]. Journal of ceramics,2014(3):291-295.
[10] OBA R,POSSAMAI T S,NICOLAU V P. Thermal analysis of a tunnel kiln used to produce roof tiles[J]. Applied thermal engineering,2014,63(1):59-65.