Deep-learning-based segmentation of fresh or young concrete sections from images of construction sites

Woldeamanuel Minwuye Mesfin, Soojin Cho, Jeongmin Lee, Hyeong Ki Kim, Taehoon Kim

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The objective of this study is to evaluate the feasibility of deep-learning-based segmentation of the area covered by fresh and young concrete in the images of construction sites. The RGB images of construction sites under various actual situations were used as an input into several types of convolutional neural network (CNN)–based segmentation models, which were trained using training image sets. Various ranges of threshold values were applied for the classification, and their accuracy and recall capacity were quantified. The trained models could segment the concrete area overall although they were not able to judge the difference between concrete of different ages as professionals can. By increasing the threshold values for the softmax classifier, the cases of incorrect prediction as concrete became almost zero, while some areas of concrete became segmented as not concrete.

Original languageEnglish
Article number6311
JournalMaterials
Volume14
Issue number21
DOIs
StatePublished - 1 Nov 2021

Keywords

  • Concrete
  • Construction site
  • Convolutional neural network (CNN)
  • Deep learning
  • Image segmentation
  • Section

Fingerprint

Dive into the research topics of 'Deep-learning-based segmentation of fresh or young concrete sections from images of construction sites'. Together they form a unique fingerprint.

Cite this