Ambiguous Surface Defect Image Classification of AMOLED Displays in Smartphones
Ambiguous Surface Defect Image Classification of AMOLED Displays in Smartphones
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DOI：
10.1109/tii.2016.2522191
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发表时间：
2016-01
影响因子：
12.3
通讯作者：
Yunwon Park;In-So Kweon
中科院分区：
文献类型：
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作者：
Yunwon Park;In-So Kweon
在本文中，我们提出了一种针对移动显示器领域广泛使用的一类显示面板模块表面上发现的形状不明确缺陷的分类方法。由于缺陷的相似性和多样性，这些类型的表面缺陷很难正确区分。在这种情况下，只能使用繁琐的人工目视检查来确定缺陷类型。为了解决表面缺陷分类不明确的问题，我们引入了一种新颖的过滤方法，可以有效地将前景缺陷区域与背景分开，该方法具有结构化图案、局部照明变化以及检查系统中多个摄像机中每个摄像机的不同光照条件。将所提出的过滤方法应用于缺陷图像，我们通过采用基于包装的特征选择方法（使用随机森林作为学习算法）来选择重要特征。使用从工业工厂的智能手机显示模块检查线收集的具有挑战性的现实世界缺陷图像数据，使用所提出的模型获得了成功的分类结果。
In this paper, we propose a classification approach for ambiguously shaped defects found on the surface of a type of display panel module that is widely used in the field of mobile displays. These types of surface defects are difficult to properly distinguish due to defect similarity and diversity. In such cases, defect types can only be determined using cumbersome human visual inspection. To solve the problem of ambiguous surface defect classification, we introduce a novel filtering method that effectively separates the foreground defective regions from the background, which has structured patterns, local illumination variation, and different light conditions for each of several cameras in an inspection system. Applying the proposed filter method to defect images, we select important features by adopting a wrapper-based feature selection method using a random forest as a learning algorithm. Successful classification results using the presented model are obtained using challenging real-world defect image data gathered from a smart phone display module inspection line in an industrial plant.