TY - GEN
T1 - CrackSense
T2 - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
AU - Wang, Liang
AU - Yang, Congying
AU - Yu, Zhiwen
AU - Liu, Yimeng
AU - Wang, Zhu
AU - Guo, Bin
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - As a common road surface distress, cracks pose a serious threat to road infrastructure and traffic safety in cities today. Consequently, road crack detection is considered as an essential step for effective road maintenance and road structure sustainability. However, due to the high cost incurred by dedicated devices and professional operators, it is impossible for existing systems to achieve universal spatiotemporal coverage across citywide road networks. To fill this gap, in this paper, we present the CrackSense, a mobile crowdsourcing based system to detect urban road crack and estimate its damage degree. Specifically, for the heterogeneous crack data, we put forward a crowdsourcing data quality evaluation and selection mechanism. And then, by utilizing the multi-source sensing data aggregation, we propose tow algorithms, namely RCTR and RCDE, to recognize road crack types, i.e., horizontal crack, vertical crack, and net crack, and estimate the crack damage degree, respectively. We implement the system and develop a smartphone APP for mobile users. By conducting intensive experiments and field study, the results demonstrate the accuracy and effectiveness of our proposed approaches.
AB - As a common road surface distress, cracks pose a serious threat to road infrastructure and traffic safety in cities today. Consequently, road crack detection is considered as an essential step for effective road maintenance and road structure sustainability. However, due to the high cost incurred by dedicated devices and professional operators, it is impossible for existing systems to achieve universal spatiotemporal coverage across citywide road networks. To fill this gap, in this paper, we present the CrackSense, a mobile crowdsourcing based system to detect urban road crack and estimate its damage degree. Specifically, for the heterogeneous crack data, we put forward a crowdsourcing data quality evaluation and selection mechanism. And then, by utilizing the multi-source sensing data aggregation, we propose tow algorithms, namely RCTR and RCDE, to recognize road crack types, i.e., horizontal crack, vertical crack, and net crack, and estimate the crack damage degree, respectively. We implement the system and develop a smartphone APP for mobile users. By conducting intensive experiments and field study, the results demonstrate the accuracy and effectiveness of our proposed approaches.
KW - Image processing
KW - Mobile crowdsourcing
KW - Road crack detection
KW - Sensors
UR - http://www.scopus.com/inward/record.url?scp=85083556817&partnerID=8YFLogxK
U2 - 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00188
DO - 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00188
M3 - 会议稿件
AN - SCOPUS:85083556817
T3 - Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
SP - 944
EP - 951
BT - Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 August 2019 through 23 August 2019
ER -