Improving Crack Detection in Concrete Structures using Augmented Data in Deep Learning Models

Forskningsoutput: KonferensbidragArbetsdokument (paper)Peer review

Sammanfattning

Abstract— While Convolutional Neural Networks are generally considered efficient structures for image processing, that task has been shown to be non-trivial in cases of datasets of imbalanced and low-quality images. Here, it is not only significant to train an algorithm towards a high accuracy enough, but also to do that in a limited amount of time. This can be understood from that low-quality datasets may require several experiments on choices of hyperparameters for an efficient network. Such experiments may be costly, not only generally in terms of time, but also in terms of payments for cloud services, as a development platform. This contribution builds on previous investigations that showed promising techniques for attacking a problem with images of cracks in bridge concrete. Results from that work is here further improved through data augmentation techniques where simply inverting images is proven to improve the performance of the network.
OriginalspråkEngelska
StatusPublicerad - 2024
EvenemangInternational Conference on Artificial Intelligence, Computer, Data Sciences and Applications - University of Seychelles, Mahe, Seychellerna
Varaktighet: 2024-feb.-01 → …
http://www.acdsa.org/

Konferens

KonferensInternational Conference on Artificial Intelligence, Computer, Data Sciences and Applications
Förkortad titelACDSA
Land/TerritoriumSeychellerna
OrtMahe
Period24-02-01 → …
Internetadress

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