CAJ | 학술논문

为了实现CTP版材表面不同类型瑕疵的自适应在线检测，引入人类视觉注意机制，融合区域生长算法对表面瑕疵图像进行分割，以面积作为特征值对瑕疵进行识别。首先根据人类视觉系统具有选择性注意机制的特性，对CTP版材表面图像的多特征图像采用图像金字塔分解得到多尺度图像，并利用基于图的算法（GBVS）将多尺度下多特征的图像融合为全局显著图。然后将显著图中最显著点即注意焦点作为区域生长的种子点，相似性作为区域生长准则，对原图像的梯度图像采用区域生长算法获得瑕疵区域二值化图像。最后利用注意抑制返回机制和邻近优先的准则，查找其他未检瑕疵，直至满足CTP版材的行业标准。实验结果表明：瑕疵检测系统分辨率0.1mm，检测平均准确率达96.3％以上，算法运行速度快，能满足CTP版材生产的在线检测实时性要求。
위료실현CTP판재표면불동류형하자적자괄응재선검측，인입인류시각주의궤제，융합구역생장산법대표면하자도상진행분할，이면적작위특정치대하자진행식별。수선근거인류시각계통구유선택성주의궤제적특성，대CTP판재표면도상적다특정도상채용도상금자탑분해득도다척도도상，병이용기우도적산법（GBVS）장다척도하다특정적도상융합위전국현저도。연후장현저도중최현저점즉주의초점작위구역생장적충자점，상사성작위구역생장준칙，대원도상적제도도상채용구역생장산법획득하자구역이치화도상。최후이용주의억제반회궤제화린근우선적준칙，사조기타미검하자，직지만족CTP판재적행업표준。실험결과표명：하자검측계통분변솔0.1mm，검측평균준학솔체96.3％이상，산법운행속도쾌，능만족CTP판재생산적재선검측실시성요구。
In order to realize automatic detection for flaw on CTP plate,an automatic detection system is established and its applied algorithms such as region growing and graph-based visual saliency(GBVS) is investigated. First,accord-ing to biological characteristics of the human visual attention mechanism,image elementary features were extracted by sampling the center-surround differences,which were combined into a saliency map. Then competition among salient points in this map gave rise to a single focus of attention (FOA) which was selected as the seed point of region grow-ing segmentation. Taking similarity for region growing criteria,binary image of flaw region was formed. Finally,after computing characteristic parameters according to flaw region and using area features,flaw on CTP plate surface were identified. Experimental results indicate that intelligent detection of flaw on CTP plate achieves system resolution of 0.1mm with average accuracy rate of 96.3%.It can satisfy the system requirements of non-contact,online,real time,high-er precision,rapid speed and stabilization.