Deep learning model for automated detection of efflorescence and its possible treatment in images of brick facades

One of the most common pathologies in exposed brick facades is efflorescence, which, although they often have a similar appearance, their effects and way of solving them can range from a one-off cleaning to a repair that involves adding or replacing the material. Therefore, the novel goal of this wo...

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Detalles Bibliográficos
Autores: Marín García, David, Bienvenido Huertas, David, Carretero Ayuso, Manuel Jesús, Della Torre, Stefano
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Consejo General de la Arquitectura Técnica de España (CGATE)
Repositorio:RIARTE
OAI Identifier:oai:www.riarte.es:20.500.12251/3308
Acceso en línea:http://hdl.handle.net/20.500.12251/3308
https://doi.org/10.1016/j.autcon.2022.104658
Access Level:acceso abierto
Palabra clave:Fachadas
Redes neuronales artificiales
Fábrica de cerámica
Patologías - Construcción
Eflorescencias
3312.05 Productos de Arcilla
3312.08 Propiedades de Los Materiales
1203.13 Cálculo Digital
3313.04 Material de Construcción
Descripción
Sumario:One of the most common pathologies in exposed brick facades is efflorescence, which, although they often have a similar appearance, their effects and way of solving them can range from a one-off cleaning to a repair that involves adding or replacing the material. Therefore, the novel goal of this work is to verify whether it is possible to automate this task of distinguishing what type of intervention each brick needs. To do this, the methodology followed focuses on proposing, training and validating a deep convolutional neural network with the real-time end-to-end method that simultaneously predicts multiple bounding boxes and class probabilities for those boxes. For this, images of 765 building facades will be used, of which 392 were selected, proceeding to label 4704 bricks, resulting in that the model achieved a mAP maximum at epoch 100 with 0.894, which is therefore of interest for the creation of intervention maps. © 2022 Elsevier B.V.