Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning

Research output: Contribution to journalArticleAcademicpeer-review

51 Downloads (Pure)

Abstract

Masonry structures represent the highest proportion of building stock worldwide. Currently, the structural condition of such structures is predominantly manually inspected which is a laborious, costly and subjective process. With developments in computer vision, there is an opportunity to use digital images to automate the visual inspection process. The aim of this study is to examine deep learning techniques for crack detection on images from masonry walls. A dataset with photos from masonry structures is produced containing complex backgrounds and various crack types and sizes. Different deep learning networks are considered and by leveraging the effect of transfer learning crack detection on masonry surfaces is performed on patch level with 95.3% accuracy and on pixel level with 79.6% F1 score. This is the first implementation of deep learning for pixel-level crack segmentation on masonry surfaces. Codes, data and networks relevant to the herein study are available in: github.com/dimitrisdais/crack_detection_CNN_masonry.
Original languageEnglish
JournalAutomation in Construction
Volume125
Early online dateFeb 2021
DOIs
Publication statusPublished - May 2021

Keywords

  • masonry
  • cnn
  • crack detection
  • segmentation
  • classification
  • transfer learning
  • deep learning
  • python
  • computer vision
  • artificial intelligence

Fingerprint

Dive into the research topics of 'Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning'. Together they form a unique fingerprint.

Cite this