Author | dc.contributor.author | López Droguett, Enrique | |
Author | dc.contributor.author | Tapia, Juan | |
Author | dc.contributor.author | Yáñez, Claudio | |
Author | dc.contributor.author | Boroschek Krauskopf, Rubén | |
Admission date | dc.date.accessioned | 2021-08-05T00:08:43Z | |
Available date | dc.date.available | 2021-08-05T00:08:43Z | |
Publication date | dc.date.issued | 2020 | |
Cita de ítem | dc.identifier.citation | Proceedings of The Institution of Mechanical Engineers Part O-Journal of Risk and Reliability Article Number 1748006X20965111 Oct 2020 | es_ES |
Identifier | dc.identifier.other | 10.1177/1748006X20965111 | |
Identifier | dc.identifier.uri | https://repositorio.uchile.cl/handle/2250/181117 | |
Abstract | dc.description.abstract | Computer vision algorithms are powerful techniques that can be used for remotely monitoring and
inspecting civil structures. Detecting and segmenting cracks in images of concrete bridges can provide
useful information related to the health of the structure. There are several states of the art methods based
on Deep Learning that have been used for segmentation tasks. However, most of them require a large
number of parameters that limits their use in mobile device applications. Here, we propose a DenseNet
architecture with only 13 layers with one feature extractor stage and two datapaths. Implementations of
state of the art semantic segmentation models are also tested. The proposed model achieves better results
than standard algorithms with only a fraction of the parameters making it suitable for developing mobile
device applications for bridge structure monitoring. As an additional contribution, two new databases for
semantic segmentation of cracks are presented. These databases are used to test all the algorithms in this
work and will be available upon request. Additional experiments using a public database are also
performed for the sake of comparison. The best results are obtained using the proposed DenseNet-13
architecture with only 350,000 parameters achieving an Intersection Over Union of 94.51% for crack
semantic segmentation. | es_ES |
Patrocinador | dc.description.sponsorship | Chilean National Fund for Scientific and Technological Development (FONDEF) | es_ES |
Lenguage | dc.language.iso | en | es_ES |
Publisher | dc.publisher | Sage | es_ES |
Source | dc.source | Proceedings of The Institution of Mechanical Engineers Part O-Journal of Risk and Reliability | es_ES |
Keywords | dc.subject | Concrete bridge | es_ES |
Keywords | dc.subject | Crack detection | es_ES |
Keywords | dc.subject | Segmentation | es_ES |
Keywords | dc.subject | Deep learning | es_ES |
Keywords | dc.subject | Structural integrity | es_ES |
Título | dc.title | Semantic segmentation model for crack images from concrete bridges for mobile devices | es_ES |
Document type | dc.type | Artículo de revista | |
dcterms.accessRights | dcterms.accessRights | Acceso a solo metadatos | es_ES |
Cataloguer | uchile.catalogador | crb | es_ES |
Indexation | uchile.index | Artículo de publicación ISI | es_ES |