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Open AccessLetter

Classification and Segmentation of Longitudinal Road Marking Using Convolutional Neural Networks for Dynamic Retroreflection Estimation

1
Future Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology (KICT), Goyang 10223, Korea
2
HI-LANDKOREA Co., Ltd., Seoul 05045, Korea
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(19), 5560; https://doi.org/10.3390/s20195560
Received: 1 September 2020 / Revised: 22 September 2020 / Accepted: 25 September 2020 / Published: 28 September 2020
Road markings constitute one of the most important elements of the road. Moreover, they are managed according to specific standards, including a criterion for a luminous contrast, which can be referred to as retroreflection. Retroreflection can be used to measure the reflection properties of road markings or other road facilities. It is essential to manage retroreflection in order to improve road safety and sustainability. In this study, we propose a dynamic retroreflection estimation method for longitudinal road markings, which employs a luminance camera and convolutional neural networks (CNNs). The images that were captured by a luminance camera were input into a classification and regression CNN model in order to determine whether the longitudinal road marking was accurately acquired. A segmentation model was also developed and implemented in order to accurately present the longitudinal road marking and reference plate if a longitudinal road marking was determined to exist in the captured image. The retroreflection was dynamically measured as a driver drove along an actual road; consequently, the effectiveness of the proposed method was demonstrated. View Full-Text
Keywords: retroreflection; longitudinal road marking; luminance; convolutional neural network; classification; segmentation retroreflection; longitudinal road marking; luminance; convolutional neural network; classification; segmentation
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MDPI and ACS Style

Chun, C.; Lee, T.; Kwon, S.; Ryu, S.-K. Classification and Segmentation of Longitudinal Road Marking Using Convolutional Neural Networks for Dynamic Retroreflection Estimation. Sensors 2020, 20, 5560. https://doi.org/10.3390/s20195560

AMA Style

Chun C, Lee T, Kwon S, Ryu S-K. Classification and Segmentation of Longitudinal Road Marking Using Convolutional Neural Networks for Dynamic Retroreflection Estimation. Sensors. 2020; 20(19):5560. https://doi.org/10.3390/s20195560

Chicago/Turabian Style

Chun, Chanjun; Lee, Taehee; Kwon, Sungil; Ryu, Seung-Ki. 2020. "Classification and Segmentation of Longitudinal Road Marking Using Convolutional Neural Networks for Dynamic Retroreflection Estimation" Sensors 20, no. 19: 5560. https://doi.org/10.3390/s20195560

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