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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Caio Henrique Esquina Limão
Federal University of Pará
Computer Science & IT Engineering

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Deep learning based slope erosion detection Caio Henrique Esquina Limão; Thabatta Moreira Alves de Araújo; Carlos Renato Lisboa Frances
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1428-1438

Abstract

Being increasingly present at the most diverse structure health monitoring (SHM) scenarios, many high-performance artificial intelligence techniques have been able to solve structural analysis problems. When it comes to image classification solutions, convolutional neural networks (CNNs) deliver the best results. This scenario encourages us to explore machine learning techniques, such as computer vision, and merge it with different technologies to achieve the best performance. This paper proposes a custom CNN architecture trained with slope erosion images that showed satisfactory results with an accuracy of 96.67%, enabling a precise and improved identification of instability indicators. These instabilities, when detected in advance, prevent disasters and enable proper maintenance to be carried out, given that its integrity directly affects structures built around and above it.
Co-Authors Carlos Renato Lisboa Frances Thabatta Moreira Alves de Araújo