Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles
For the slate tile industry, this work improves production efficiency and quality control by automating visual inspection of natural materials.
The paper presents a hybrid deep learning method combining XFeat with LightGlue and MobileNetV3 for reidentification and classification of slate tiles, achieving +15.4% AUC improvement in instance matching and +10.9% accuracy improvement over a standard MobileNetV3 on a new industrial dataset of 2,610 images.
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.