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2022 | OriginalPaper | Buchkapitel

Transformers with YOLO Network for Damage Detection in Limestone Wall Images

verfasst von : Koubouratou Idjaton, Xavier Desquesnes, Sylvie Treuillet, Xavier Brunetaud

Erschienen in: Image Analysis and Processing. ICIAP 2022 Workshops

Verlag: Springer International Publishing

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Abstract

Cultural heritage buildings damage detection is of a great significance for planning restoration operations. However, the buildings analysis is generally performed by experts through on-site qualitative visual assessments. A highly time-consuming task, hardly possible at the scale of large historical buildings.
This paper proposes a new neural network architecture for automatic detection of spalling zones in limestone walls with color images. This architecture consists of the latest YOLO network, enhanced with layers of transformers encoder providing more comprehensive features. The performances of the proposed network improve significantly those of the YOLO core network on our dataset of over 1000 high resolution images from the Renaissance style Château de Chaumont in the Loire Valley (France).

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Metadaten
Titel
Transformers with YOLO Network for Damage Detection in Limestone Wall Images
verfasst von
Koubouratou Idjaton
Xavier Desquesnes
Sylvie Treuillet
Xavier Brunetaud
Copyright-Jahr
2022
DOI
https://doi.org/10.1007/978-3-031-13324-4_26