UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks

被引：25

作者：

Choi, Daegyun ^{[1
]}

Bell, William ^{[2
]}

Kim, Donghoon ^{[1
]}

Kim, Jichul ^{[3
]}

机构：

[1] Univ Cincinnati, Dept Aerosp Engn & Engn Mech, Cincinnati, OH 45221 USA

[2] Dynetics, Huntsville, AL 35806 USA

[3] Mississippi State Univ, Dept Aerosp Engn, Mississippi State, MS 39759 USA

来源：

SENSORS | 2021年 / 21卷 / 08期

关键词：

crack detection; deep learning; convolutional neural network; image processing; unmanned aerial vehicle; INSPECTION;

D O I：

10.3390/s21082650

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

Structural cracks are a vital feature in evaluating the health of aging structures. Inspectors regularly monitor structures' health using visual information because early detection of cracks on highly trafficked structures is critical for maintaining the public's safety. In this work, a framework for detecting cracks along with their locations is proposed. Image data provided by an unmanned aerial vehicle (UAV) is stitched using image processing techniques to overcome limitations in the resolution of cameras. This stitched image is analyzed to identify cracks using a deep learning model that makes judgements regarding the presence of cracks in the image. Moreover, cracks' locations are determined using data from UAV sensors. To validate the system, cracks forming on an actual building are captured by a UAV, and these images are analyzed to detect and locate cracks. The proposed framework is proven as an effective way to detect cracks and to represent the cracks' locations.

引用

页数：20

共 43 条

[31]

Simard PY, 2003, PROC INT CONF DOC, P958

[32]

Simonyan K., 2015, P INT C LEARN REPR I, P1

[33]

Soukup D, 2014, LECT NOTES COMPUT SC, V8887, P668, DOI 10.1007/978-3-319-14249-4_64

[34] Using GPUs for machine learning algorithms [J].