TY - GEN
T1 - 3Pod
T2 - 8th IEEE International Smart Cities Conference, ISC2 2022
AU - Alshammari, Sami
AU - Song, Sejun
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The roadway is the backbone of the country. Bad road conditions can cause vehicles damages and create hazardous driving conditions. Auto damages caused by potholes can add up to thousands of dollars per vehicle. The problem is supposed to be more significant to the auto-driving cars. Therefore, evaluating and maintaining roads of road defects plays an essential role in the economy. Most cities rely on residents to report those conditions. However, aside from being a burden on residents, it may not be an effective method. Automating road defect detection using computer vision techniques would significantly ensure roads remain safe and efficient. Smart cities leverage physical and virtual technologies that rely on sensors and cloud-based communication to improve urban environments. This paper proposes a federated deep learning-based 3 Dimensional (3D) pothole detection (3Pod), which is an intelligent real-time evaluation and reporting platform of road conditions and MRI (Maintenance Responsiveness Indicator) using IoT and Artificial Intelligence technologies. It detects road defects in 3D with size estimation to discern other road objects, including patched potholes, fake road bumps, etc. Furthermore, it provides an avoidability score for each defect to show its risk to commuters on the road and their vehicles. We also propose a crowd-voting technique to calculate MRI (Maintenance Responsiveness Indicator), which helps evaluate maintenance performance.
AB - The roadway is the backbone of the country. Bad road conditions can cause vehicles damages and create hazardous driving conditions. Auto damages caused by potholes can add up to thousands of dollars per vehicle. The problem is supposed to be more significant to the auto-driving cars. Therefore, evaluating and maintaining roads of road defects plays an essential role in the economy. Most cities rely on residents to report those conditions. However, aside from being a burden on residents, it may not be an effective method. Automating road defect detection using computer vision techniques would significantly ensure roads remain safe and efficient. Smart cities leverage physical and virtual technologies that rely on sensors and cloud-based communication to improve urban environments. This paper proposes a federated deep learning-based 3 Dimensional (3D) pothole detection (3Pod), which is an intelligent real-time evaluation and reporting platform of road conditions and MRI (Maintenance Responsiveness Indicator) using IoT and Artificial Intelligence technologies. It detects road defects in 3D with size estimation to discern other road objects, including patched potholes, fake road bumps, etc. Furthermore, it provides an avoidability score for each defect to show its risk to commuters on the road and their vehicles. We also propose a crowd-voting technique to calculate MRI (Maintenance Responsiveness Indicator), which helps evaluate maintenance performance.
KW - Federated learning
KW - machine learning
KW - objects detection
KW - potholes detection
KW - road quality monitoring
UR - http://www.scopus.com/inward/record.url?scp=85142015414&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142015414&partnerID=8YFLogxK
U2 - 10.1109/ISC255366.2022.9922195
DO - 10.1109/ISC255366.2022.9922195
M3 - Conference contribution
AN - SCOPUS:85142015414
T3 - ISC2 2022 - 8th IEEE International Smart Cities Conference
BT - ISC2 2022 - 8th IEEE International Smart Cities Conference
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 September 2022 through 29 September 2022
ER -