Smart-Inspect: Micro Scale Localization and Classification of Smartphone Glass Defects for Industrial Automation

TL;DR

Smart-Inspect introduces a semi-supervised learning framework for micro-scale localization and classification of smartphone glass defects, outperforming manual inspection and existing algorithms in accuracy and depth detection.

Contribution

The paper presents a novel semi-supervised model that accurately detects and classifies tiny defects on smartphone glass, differentiating defects from reflections and dust with high robustness.

Findings

Outperforms PCA and fusion algorithms in defect classification

Achieves defect detection at depths up to 5 microns

Outperforms manual inspection in accuracy

Abstract

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.