TY - GEN
T1 - A Deep Learning-Based Approach for Road Pothole Detection in Timor Leste
AU - Pereira, Vosco
AU - Tamura, Satoshi
AU - Hayamizu, Satoru
AU - Fukai, Hidekazu
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/28
Y1 - 2018/9/28
N2 - This research proposes a low-cost solution for detecting road potholes image by using convolutional neural network (CNN). Our model is trained entirely on the image which collected from several different places and has variation such as in wet, dry and shady conditions. The experiment using the 500 testing images showed that our model can achieve (99.80 %) of Accuracy, Precision (100%), Recall (99.60%), and F-Measure (99.60%) simultaneously.
AB - This research proposes a low-cost solution for detecting road potholes image by using convolutional neural network (CNN). Our model is trained entirely on the image which collected from several different places and has variation such as in wet, dry and shady conditions. The experiment using the 500 testing images showed that our model can achieve (99.80 %) of Accuracy, Precision (100%), Recall (99.60%), and F-Measure (99.60%) simultaneously.
KW - Convolutional Neural Network
KW - Deep Learning
KW - Image Classification
KW - Potholes
UR - http://www.scopus.com/inward/record.url?scp=85055654637&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055654637&partnerID=8YFLogxK
U2 - 10.1109/SOLI.2018.8476795
DO - 10.1109/SOLI.2018.8476795
M3 - Conference contribution
AN - SCOPUS:85055654637
T3 - Proceedings of the 2018 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2018
SP - 279
EP - 284
BT - Proceedings of the 2018 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2018
Y2 - 31 July 2018 through 2 August 2018
ER -