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| A Method for Classifying Stains and Defects on Mobile Phone Cover Glass Based on Hyperspectral Line Scan Imaging |
| SHEN Guan-ting1, RAO Ke-yi1, FANG Rui-xin1, ZHANG Xue-min1, WU Zhao-cong1, 2* |
1. School of Remote Sensing Information Engineering，Wuhan University，Wuhan 430072，China
2. Aerospace Information Research Institute, Henan Academy of Sciences, Zhengzhou 450046, China
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Abstract Cover glass is an important component of intelligent terminal products, with a smooth, transparent surface and complex and variable characteristics. Its appearance inspection is one of the important challenges in optical imaging detection. Currently, conventional detection methods are mainly based on visible light images. Still, due to texture similarity, stains are often misjudged as defects, making good products judged as defective, thereby increasing industrial costs. To overcome the above problems, this article proposes a method for detecting stains and defects on cover glass based on hyperspectral technology. This method selects the hyperspectral data's optimal feature spectrum and establishes a quantitative detection model to select key feature bands and accurately detect stains and defects. This article utilizes the optical properties of the ink and AA transparent areas and adopts a linear light source transmission imaging method. Through professional hyperspectral line scanning equipment, 50 hyperspectral images of mobile phone cover glass were effectively collected. A stain defect dataset was created, including 500 samples of clean and flawless cover plates and 100 samples of four types of stains and scratch defects, including glass fingerprints, adhesive substances, cleaning agents, and dust. Based on the above hyperspectral image dataset, this paper constructs a band selection method that comprehensively considers the spectral characteristics of stains and defects and the contribution and importance of each feature band. Eight feature bands (502, 526, 567, 689, 711, 789, 818, and 888 nm) that can effectively distinguish stains and defects are selected. Using machine learning algorithms for detection, experimental results show that 8 selected hyperspectral bands perform well in distinguishing stains and defects, with an accuracy rate of 95.4% and an error rate of only 4.7%. Hyperspectral imaging can capture the differences between defects and stains in the spectrum, achieving more accurate detection and providing a feasible new method for quality inspection of mobile phone cover glass. It can provide a reference for designing low-cost hyperspectral defect and stain detection cameras in practical applications.
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Received: 2023-11-14
Accepted: 2024-07-03
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Corresponding Authors:
WU Zhao-cong
E-mail: zcwoo@whu.edu.cn
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