A Hierarchical hybrid architecture with CNN autoencoder for foreign object detection on jig surfaces in smartphone screen manufacturing
Year: 2026
Type: article
Abstract: Foreign object and surface defect detection on inspection jigs is critical in smartphone screen manufacturing, demanding both real-time speed and high accuracy. This paper proposes a Hierarchical Hybrid Pipeline comprising two phases: Phase 1 employs traditional image processing with rigid Euclidean alignment, absolute background subtraction, and robust-statistics-based dynamic thresholding (Median + k × MAD) for high-speed full-frame screening; Phase 2 employs an unsupervised CNN Autoencoder with Ensemble anomaly scoring (Local MSE + Feature-based MSE) for in-depth verification of suspect regions. Evaluated on a dataset collected from an actual Lamination production line, Phase 1 achieves 99.25% Accuracy on 1,200 images (120–200 ms processing time). Phase 2 attains an AUC-ROC of 0.971 and F1-score of 0.961 with 97.1% Recall. The complete pipeline executes within 300–350 ms on industrial CPU hardware, satisfying real-time production requirements.
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Funder: Samsung
OpenAlex ID: W7220575054