# Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles

arXiv: https://arxiv.org/abs/2607.04811 · HTML: https://mlsanity.com/paper/2607.04811/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 6 Jul 2026.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [RoMa: Robust Dense Feature Matching](https://mlsanity.com/paper/2305.15404/) (2023)
- [XFeat: Accelerated Features for Lightweight Image Matching](https://mlsanity.com/paper/2404.19174/) (2024)
- [GlueStick: Robust Image Matching by Sticking Points and Lines Together](https://mlsanity.com/paper/2304.02008/) (2023)
- [Steerers: A framework for rotation equivariant keypoint descriptors](https://mlsanity.com/paper/2312.02152/) (2023)

## Abstract (arXiv)

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.