轻量级混合模型提升石材溯源与分类准确率
Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles
- 融合XFeat特征匹配与MobileNetV3分类，统一框架处理识别与分类
- 实例匹配AUC提升15.4%，分类准确率提高10.9%（对比标准MobileNetV3）
- 适用于工业场景中天然石材的自动化质检与来源追踪
将深度学习应用于板岩瓷砖的实例感知重识别与开采地分类，可提升板岩瓷砖行业的生产效率与质量控制。该任务对天然材料尤为重要，因其视觉差异大，人工检测成本高且易出错。本文提出一种轻量级混合深度学习方法，将图像匹配与分类集成于同一框架中。系统结合基于XFeat的特征匹配分支与基于MobileNetV3的分类分支。XFeat分支配合LightGlue匹配头，使实例匹配性能提升15.4% AUC。分类部分共享并融合双主干特征，较标准MobileNetV3模型准确率提升10.9%。该方法在新构建的工业数据集上进行评估，包含来自六个开采地的2,610张板岩瓷砖图像。结果表明，该方法在工业场景下的物体重识别与分类任务中具有显著有效性。
原文摘要 · Abstract (English)
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.
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