TY - GEN
T1 - Semantic Segmentation of Paved Road and Pothole Image Using U-Net Architecture
AU - Pereira, Vosco
AU - Tamura, Satoshi
AU - Hayamizu, Satoru
AU - Fukai, Hidekazu
N1 - Funding Information:
The authors would like to thanks to the lecturers and staff of Faculty of Engineering and Science and Technology of National University of Timor Leste and Gifu University Japan. Special thanks to Japan International Cooperation Agency (JICA) for funding this research through the project of CADE-FEST phase 2.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - Research on road monitoring system has been actively conducted by using both machine learning and deep learning technique. One of our nearest goal in the framework of road condition monitoring system is to segment all road related object and provide a technical report regarding road condition. Our final objective is to develop a community participant-based system for road condition monitoring. As one of our task, in this research, we start with the segmentation of road and pothole. To conduct this task we proposed a semantic segmentation method for road and pothole image segmentation by using one of the famous deep learning technique U-Net. Various condition of road images were used for training and validating the model. The experiment result showed that U-Net model can achieve 97 % of accuracy and 0.86 of mean Intersection Over Union (mIOU).
AB - Research on road monitoring system has been actively conducted by using both machine learning and deep learning technique. One of our nearest goal in the framework of road condition monitoring system is to segment all road related object and provide a technical report regarding road condition. Our final objective is to develop a community participant-based system for road condition monitoring. As one of our task, in this research, we start with the segmentation of road and pothole. To conduct this task we proposed a semantic segmentation method for road and pothole image segmentation by using one of the famous deep learning technique U-Net. Various condition of road images were used for training and validating the model. The experiment result showed that U-Net model can achieve 97 % of accuracy and 0.86 of mean Intersection Over Union (mIOU).
KW - Deep Learning
KW - Pothole
KW - Road
KW - Road Condition Monitoring
KW - Semantic Segmentation
KW - U-Net
UR - http://www.scopus.com/inward/record.url?scp=85084070391&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084070391&partnerID=8YFLogxK
U2 - 10.1109/ICAICTA.2019.8904105
DO - 10.1109/ICAICTA.2019.8904105
M3 - Conference contribution
AN - SCOPUS:85084070391
T3 - Proceedings - 2019 International Conference on Advanced Informatics: Concepts, Theory, and Applications, ICAICTA 2019
BT - Proceedings - 2019 International Conference on Advanced Informatics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Advanced Informatics: Concepts, Theory, and Applications, ICAICTA 2019
Y2 - 20 September 2019 through 22 September 2019
ER -