Abstract Defect detection is a critical way for quality ensuring of mobile phone screens. In this paper, we propose a novel defect extraction and classification scheme for mobile phone screen based on machine vision. In order to improve the efficiency of the algorithm, a pre-examination algorithm and a coarse-precise defect extraction strategy are designed. Considering the problem that there are various types of mobile phone screen, a region of interest (ROI) acquisition algorithm is proposed to ensure the universality of the detection method. Besides, a clustering algorithm is proposed to avoid false detection or missed detection of cluster defects. Furthermore, the detection criteria are defined, and a classification algorithm combining multi-layer perceptron (MLP) and deep learning (DL) technologies is proposed. Experimental results demonstrate that satisfactory performance is achieved in detecting scratches, floaters, light stains and dark stains of the mobile phone screen with the proposed detection scheme.

中文翻译：

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一种基于机器视觉的手机屏幕缺陷提取与分类新算法

摘要 缺陷检测是手机屏幕质量保证的重要手段。在本文中，我们提出了一种新的基于机器视觉的手机屏幕缺陷提取和分类方案。为了提高算法的效率，设计了预检算法和粗精缺陷提取策略。考虑到手机屏幕种类繁多的问题，提出了一种感兴趣区域（ROI）获取算法，以保证检测方法的通用性。此外，还提出了一种聚类算法，以避免对聚类缺陷的误检或漏检。此外，定义了检测标准，并提出了一种结合多层感知器（MLP）和深度学习（DL）技术的分类算法。