Construction material classification on imbalanced datasets for
construction monitoring automation using Vision Transformer (ViT)
architecture
- URL: http://arxiv.org/abs/2108.09527v1
- Date: Sat, 21 Aug 2021 15:29:56 GMT
- Title: Construction material classification on imbalanced datasets for
construction monitoring automation using Vision Transformer (ViT)
architecture
- Authors: Maryam Soleymani, Mahdi Bonyani, Hadi Mahami, Farnad Nasirzadeh
- Abstract summary: The scope of automation in construction includes a wide range of stages, and monitoring construction projects is no exception.
In this paper, a novel deep learning architecture is utilized, called Vision Transformer (ViT), for detecting and classifying construction materials.
The achieved results revealed an accuracy of 100 percent in all parameters and also in each material category.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Nowadays, automation is a critical topic due to its significant impacts on
the productivity of construction projects. Utilizing automation in this
industry brings about great results, such as remarkable improvements in the
efficiency, quality, and safety of construction activities. The scope of
automation in construction includes a wide range of stages, and monitoring
construction projects is no exception. Additionally, it is of great importance
in project management since an accurate and timely assessment of project
progress enables managers to quickly identify deviations from the schedule and
take the required actions at the right time. In this stage, one of the most
important tasks is to daily keep track of the project progress, which is very
time-consuming and labor-intensive, but automation has facilitated and
accelerated this task. It also eliminated or at least decreased the risk of
many dangerous tasks. In this way, the first step of construction automation is
to detect used materials in a project site automatically. In this paper, a
novel deep learning architecture is utilized, called Vision Transformer (ViT),
for detecting and classifying construction materials. To evaluate the
applicability and performance of the proposed method, it is trained and tested
on three large imbalanced datasets, namely Construction Material Library (CML)
and Building Material Dataset (BMD), used in the previous papers, as well as a
new dataset created by combining them. The achieved results revealed an
accuracy of 100 percent in all parameters and also in each material category.
It is believed that the proposed method provides a novel and robust tool for
detecting and classifying different material types.
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