数字孪生(DTs) 允许道路基础设施检查, 数字化，尽管这受到有限注释数据的阻碍。这项工作利用连续资产状况监控的关系性质，将基于图像的缺陷检测重新表述为图像差异分类(IDC)，以减少数据依赖。这是在使用新策划的, 高质量数据集使用不同 IDC 分类器进行低资源交通标志检查的案例研究中对此进行评估的。结果表明，基于指令的分类器优于基于编码器的分类器，并且通过与参考图像的比较获得了收益。这表明IDC可以成为解决基础设施检查和DT资产状况更新中的数据约束的有效任务建模。
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.
科目:人工智能(cs.AI)
Subjects: Artificial Intelligence (cs.AI)