TY - GEN
T1 - Classification of Paved and Unpaved Road Image Using Convolutional Neural Network for Road Condition Inspection System
AU - Pereira, Vosco
AU - Tamura, Satoshi
AU - Hayamizu, Satoru
AU - Fukai, Hidekazu
N1 - Funding Information:
The authors would like to express gratitude for the help of other lecturers and staffs in the Faculty of Engineering Science and Technology, National University of Timor Leste. This research was funded and supported by Japan International Cooperation Agency (JICA) through the Capacity Development for Faculty of Engineering Science and Technology (CADEFEST) 2 project.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/20
Y1 - 2018/11/20
N2 - Image processing techniques have been actively used for research on road condition inspection and achieving high detection accuracies. Many studies focus on the detection of cracks and potholes of the road. However, in some least developed countries, there are some distances of roads are still unpaved and it escaped the attention of the researchers. Inspired by penetration and success in applying deep learning technic to computer vision and to any other fields and by the existence of the various type of smartphone devices, we proposed a low - cost method for paved and unpaved road images classification using convolutional neural network (CNN). Our model is trained with 13.186 images and validate with 3.186 images which collected using smartphone device in various conditions of roads such as wet, muddy, dry, dusty and shady conditions and with different types of road surface such as ground, rocks and sands. The experiment using 500 new testing images showed that our model can achieve high Precision (98.0%), Recall (98.4%) and F1 - Score (98.2%) simultaneously.
AB - Image processing techniques have been actively used for research on road condition inspection and achieving high detection accuracies. Many studies focus on the detection of cracks and potholes of the road. However, in some least developed countries, there are some distances of roads are still unpaved and it escaped the attention of the researchers. Inspired by penetration and success in applying deep learning technic to computer vision and to any other fields and by the existence of the various type of smartphone devices, we proposed a low - cost method for paved and unpaved road images classification using convolutional neural network (CNN). Our model is trained with 13.186 images and validate with 3.186 images which collected using smartphone device in various conditions of roads such as wet, muddy, dry, dusty and shady conditions and with different types of road surface such as ground, rocks and sands. The experiment using 500 new testing images showed that our model can achieve high Precision (98.0%), Recall (98.4%) and F1 - Score (98.2%) simultaneously.
KW - Convolutional Neural Network
KW - Deep Learning
KW - Image Classification
KW - Paved
KW - Road Condition Inspec-tion
KW - Unpaved
UR - http://www.scopus.com/inward/record.url?scp=85059939141&partnerID=8YFLogxK
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U2 - 10.1109/ICAICTA.2018.8541284
DO - 10.1109/ICAICTA.2018.8541284
M3 - Conference contribution
AN - SCOPUS:85059939141
T3 - ICAICTA 2018 - 5th International Conference on Advanced Informatics: Concepts Theory and Applications
SP - 165
EP - 169
BT - ICAICTA 2018 - 5th International Conference on Advanced Informatics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Advanced Informatics: Concepts Theory and Applications, ICAICTA 2018
Y2 - 14 August 2018 through 17 August 2018
ER -