用图像差异分类解决交通标志检测数据少的问题
Rethinking Infrastructure Inspection as Image Difference Classification: A Traffic Sign Case Study
- 将缺陷检测转为图像差异分类，减少对标注数据的依赖
- 基于指令的分类器表现优于编码器模型，对比参考图有提升
- 适合低资源场景下的基础设施状态监测与数字孪生更新
数字孪生（DT）推动道路基础设施数字化巡检，但受限于标注数据不足。本文利用资产状态持续监控的关联性，将基于图像的缺陷检测重构为图像差异分类（IDC），以降低数据依赖。在低资源交通标志检测案例中，使用新构建的高质量数据集评估了多种IDC分类器。结果表明，基于指令的分类器性能优于编码器类模型，并通过与参考图像对比获得增益。这说明IDC可作为应对基础设施巡检数据约束的有效任务建模方式，适用于数字孪生资产状态更新。
原文摘要 · Abstract (English)
Digital twins (DTs) allow the digitalization of road infrastructure inspection, though this is hindered by limited annotated data. This work exploits the relational nature of continuous asset condition monitoring to reformulate image-based defect detection as image difference classification (IDC) to reduce data reliance. This was evaluated in a case study on low-resource traffic sign inspection with different IDC classifiers using a newly-curated, high quality dataset. Results indicate that the instruction-based classifier outperforms encoder-based ones and gains from comparison with reference images. This shows that IDC can be an effective task modeling for tackling data constraints in infrastructure inspection and DT asset condition updating.
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