TY - GEN
T1 - Detecting Type and Size of Road Crack with the Smartphone
AU - Kong, Yingying
AU - Yu, Zhiwen
AU - Chen, Huihui
AU - Wang, Zhu
AU - Chen, Chao
AU - Guo, Bin
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/8
Y1 - 2017/8/8
N2 - Detecting crack type and crack size is crucial for road maintenance and management. Mobile crowd sensing is a new way to collect the information of cracks on roads. We propose a system named CrackDetector to detect cracks and estimate their types and size with smart phone in this paper. The type of a crack (i.e., horizontal crack, vertical crack, net crack) is determined by a coordinate transmission method based on three directions, including the direction of the crack in the photo (estimated through an image processing method), the 3D shooting direction (i.e., the rotation of the smartphone while photographing) and the direction of the road (obtained from the OpenStreetMap road network). In order to estimate the size of a crack (i.e., the width and the length of the crack), we use the camera's convex lens imaging theory and readings of the accelerometer and the magnetometer sensor. We collected pictures of 152 cracks with a specifically-developed android application by 8 volunteers. Experimental results show that our approach achieves an accuracy of 90.1% in crack type detection. Meanwhile, we get a 3.2cm RMSE error when estimating the crack width and a 13.2cm RMSE error when estimating the crack length.
AB - Detecting crack type and crack size is crucial for road maintenance and management. Mobile crowd sensing is a new way to collect the information of cracks on roads. We propose a system named CrackDetector to detect cracks and estimate their types and size with smart phone in this paper. The type of a crack (i.e., horizontal crack, vertical crack, net crack) is determined by a coordinate transmission method based on three directions, including the direction of the crack in the photo (estimated through an image processing method), the 3D shooting direction (i.e., the rotation of the smartphone while photographing) and the direction of the road (obtained from the OpenStreetMap road network). In order to estimate the size of a crack (i.e., the width and the length of the crack), we use the camera's convex lens imaging theory and readings of the accelerometer and the magnetometer sensor. We collected pictures of 152 cracks with a specifically-developed android application by 8 volunteers. Experimental results show that our approach achieves an accuracy of 90.1% in crack type detection. Meanwhile, we get a 3.2cm RMSE error when estimating the crack width and a 13.2cm RMSE error when estimating the crack length.
KW - crack size
KW - crack type
KW - Mobile crowd sensing
KW - smartphone sensing
UR - http://www.scopus.com/inward/record.url?scp=85034632038&partnerID=8YFLogxK
U2 - 10.1109/CSE-EUC.2017.106
DO - 10.1109/CSE-EUC.2017.106
M3 - 会议稿件
AN - SCOPUS:85034632038
T3 - Proceedings - 2017 IEEE International Conference on Computational Science and Engineering and IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, CSE and EUC 2017
SP - 572
EP - 579
BT - Proceedings - 2017 IEEE International Conference on Computational Science and Engineering and IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, CSE and EUC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Computational Science and Engineering and 15th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, CSE and EUC 2017
Y2 - 21 July 2017 through 24 July 2017
ER -