aiqbench analysis
This study proposes reframing infrastructure defect detection as image difference classification to mitigate data scarcity in digital twin applications. By leveraging reference images, instruction-based classifiers demonstrate superior performance over encoder-based models in low-resource traffic sign inspection scenarios. The approach offers a practical pathway for reducing annotation dependencies in continuous asset condition monitoring.
Rethinking Infrastructure Inspection as Image Difference Classification: A Traffic Sign Case Study
arXiv:2606.06375v1 Announce Type: new Abstract: 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.
Abstract: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.
Artificial Intelligence (cs.AI)
arXiv:2606.06375 [cs.AI]
(or arXiv:2606.06375v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.06375 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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