スマートフォンガラス欠陥のマイクロスケール位置特定と分類のための半教師あり学習フレームワーク
Smart-Inspect: Micro Scale Localization and Classification of Smartphone Glass Defects for Industrial Automation
16K画素のスマートフォンガラス画像から、傷・ひびによる光漏れ・ピットを半教師あり学習で高精度に検出・分類し、5ミクロンまでの検査深度を実現した。
著者: M Usman Maqbool Bhutta, Shoaib Aslam, Peng Yun, Jianhao Jiao, Ming Liu
分類: cs.CV, cs.RO
原文アブストラクト
The presence of any type of defect on the glass screen of smart devices has a great impact on their quality. We present a robust semi-supervised learning framework for intelligent micro-scaled localization and classification of defects on a 16K pixel image of smartphone glass. Our model features the efficient recognition and labeling of three types of defects: scratches, light leakage due to cracks, and pits. Our method also differentiates between the defects and light reflections due to dust particles and sensor regions, which are classified as non-defect areas. We use a partially labeled dataset to achieve high robustness and excellent classification of defect and non-defect areas as compared to principal components analysis (PCA), multi-resolution and information-fusion-based algorithms. In addition, we incorporated two classifiers at different stages of our inspection framework for labeling and refining the unlabeled defects. We successfully enhanced the inspection depth-limit up to 5 microns. The experimental results show that our method outperforms manual inspection in testing the quality of glass screen samples by identifying defects on samples that have been marked as good by human inspection.
