A novel algorithm for defect extraction and classification of mobile phone screen based on machine vision

被引：36

作者：

Li, Changsheng ^{[1
]}

Zhang, Xianmin ^{[1
]}

Huang, Yanjiang ^{[1
]}

Tang, Chuangang ^{[1
]}

Fatikow, Sergej ^{[1
,2
]}

机构：

[1] South China Univ Technol, Guangdong Key Lab Precis Equipment & Mfg Technol, Guangzhou 510640, Peoples R China

[2] Carl von Ossietzky Univ Oldenburg, Div Microrobot & Control Engn, D-26129 Oldenburg, Germany

来源：

COMPUTERS & INDUSTRIAL ENGINEERING | 2020年 / 146卷

基金：

中国国家自然科学基金;

关键词：

Mobile phone screen; Defect detection; Machine vision; deep learning; SURFACE; INSPECTION;

D O I：

10.1016/j.cie.2020.106530

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页数：14