Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning

By Dongho Kang, Sukhpreet S. Benipal, Dharshan Lokekere Gopal, Young-Jin Cha. 2020

367 citations recorded by the source corpus.

Abstract This paper proposes an automatic crack detection, localization, and quantification method using the integration of a faster region proposal convolutional neural network (Faster R-CNN) algorithm to detect crack regions. The regions were located using various bounding boxes and a modified tubularity flow field (TuFF) algorithm to segment the crack pixels from the detected crack regions. A modified distance transform method (DTM) was used to measure crack thickness and length in terms of pixel measurement. To validate the proposed method, 100 images were taken in different places with complex backgrounds containing different angles and distances between the camera and the objects. The results obtained from the Faster-R-CNN-based crack damage detection had a 95% average precision. The pixel-level segmentation performance of the modified TuFF algorithm exhibited an authentic outcome, with 83% intersection over union. Finally, the modified DTM algorithm provided 93% accuracy with respect to crack length and thickness with a 2.6 pixel root mean square error.

Subfields: Civil and Structural Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/52a67032be1964a34381b4ad93dbbdffac03712e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. D. Dais
2. Vasilis Sarhosis
3. E. Smyrou
4. İ. Bal

In Mechanical Engineering

1. D. Dais
2. Vasilis Sarhosis
3. E. Smyrou
4. İ. Bal

Citations in the corpus

• Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning (2021, methodology): “As stated by Kang et al. [44], the proposed method is useful for concrete surfaces only and its applicability on different materials might be limited Moreover, Chen et al. [45] implemented an encoder-decoder architecture and proposed a switch method to distinguish between the negative and positive…”