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A Framework for Forming Middle Distance Routes Based on Spatial Guidelines, Perceived Accessibility and Visual Cues in Smart City Read chapter Read first chapter

2022 | OriginalPaper | Chapter

Transformers with YOLO Network for Damage Detection in Limestone Wall Images

Authors : Koubouratou Idjaton, Xavier Desquesnes, Sylvie Treuillet, Xavier Brunetaud

Published in: Image Analysis and Processing. ICIAP 2022 Workshops

Publisher: 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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previous chapter PergaNet: A Deep Learning Framework for Automatic Appearance-Based Analysis of Ancient Parchment Collections
next chapter On the Effectiveness of 3D Vision Transformers for the Prediction of Prostate Cancer Aggressiveness
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Metadata
Title
Transformers with YOLO Network for Damage Detection in Limestone Wall Images
Authors
Koubouratou Idjaton
Xavier Desquesnes
Sylvie Treuillet
Xavier Brunetaud
Copyright Year
2022
Book
Image Analysis and Processing. ICIAP 2022 Workshops

Print ISBN: 978-3-031-13323-7

Electronic ISBN: 978-3-031-13324-4

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-031-13324-4_26

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