Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles

2026-07-06

AI Summary

This study addresses the high cost and error-proneness of manual inspection of natural slate tiles, which exhibit significant visual variability. To tackle this challenge, the authors propose a lightweight hybrid deep learning framework that, for the first time, integrates XFeat and MobileNetV3 within a unified architecture. The model employs a dual-branch feature sharing and fusion mechanism to jointly optimize instance re-identification and quarry origin classification. Specifically, XFeat coupled with LightGlue enables high-precision image matching, while MobileNetV3 handles source classification. Evaluated on a newly curated industrial dataset comprising 2,610 tile images, the proposed method achieves a 15.4% improvement in instance matching AUC and a 10.9% increase in classification accuracy over the standard MobileNetV3 baseline.

Abstract

Applying deep learning to instance-aware reidentification of slate tiles and extraction site classification can improve production efficiency and quality control in the slate tile industry. These tasks are particularly important for handling natural materials where visual variability can make manual inspection costly and error-prone. We present a lightweight, hybrid deep learning approach that combines image matching and classification within a single framework. The system integrates a feature-matching branch based on XFeat with a MobileNetV3- based classification branch. The XFeat branch, combined with a LightGlue matching head, improves instance matching performance by +15.4% AUC. For classification, features from both backbones are shared and fused, resulting in a +10.9% accuracy improvement over a standard MobileNetV3 model. Our approach is evaluated on a newly created industrial dataset consisting of 2,610 slate tile images from six extraction sites. The results demonstrate the effectiveness of the proposed approach for object re-identification and classification in an industrial setting.

Problem

• traceability
• classification
• slate tiles
• instance re-identification
• industrial inspection

Innovation

• hybrid deep learning
• instance re-identification
• feature matching
• lightweight architecture
• industrial classification