Pavement Distress Classification Using Deep Learning Method Based on Digital Image

DOI

10.2991/aer.k.200220.030How to use a DOI?

Keywords

distress classification, deep learning, image processing, You Only Look Once

Abstract

Maintaining the road regularly is a necessity, because the road is a vital infrastructure. One of automatic road maintenance steps is the detection of road distress type. Several methods have been used to detect and classify road distress automatically. This research determines the existence and classifies the deterioration of pavement using the Deep Learning method. The type of road distress detected are potholes, line-cracks, and non-line cracks. In this study, the deep learning method implemented is You Only Look Once (YOLO). The YOLO method uses Convolutional Neural Network (CNN) in its architecture and has given good results in object detection both on images and videos. YOLO has been tested in various datasets and given faster and accurate results. In this research, the pre-processing steps are cropping and resizing images then annotating the data. After that, the training is done by fine-tuning YOLO network process. The YOLO architecture uses 9-layer convolution and six layer maxpool. The testing results for datasets show that the highest accuracy is 99% and the highest average IoU is 75,1%. The run time of classification is 0.883 seconds per image.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Dwi Ratna Sulistyaningrum
AU  - Daniel Oranova
AU  - Ravy Hayu Pramestya
AU  - Imam Mukhlash
AU  - Budi Setiyono
AU  - Ervina Ahyudanari
PY  - 2020
DA  - 2020/02/25
TI  - Pavement Distress Classification Using Deep Learning Method Based on Digital Image
BT  - Proceedings of the 2nd International Symposium on Transportation Studies in Developing Countries (ISTSDC 2019)
PB  - Atlantis Press
SP  - 143
EP  - 146
SN  - 2352-5401
UR  - https://doi.org/10.2991/aer.k.200220.030
DO  - 10.2991/aer.k.200220.030
ID  - Sulistyaningrum2020
ER  -