Next Article in Journal
DeepMoCap: Deep Optical Motion Capture Using Multiple Depth Sensors and Retro-Reflectors
Next Article in Special Issue
Visible-Light Camera Sensor-Based Presentation Attack Detection for Face Recognition by Combining Spatial and Temporal Information
Previous Article in Journal
Magnetometer Calibration and Field Mapping through Thin Plate Splines
Previous Article in Special Issue
Faster R-CNN and Geometric Transformation-Based Detection of Driver’s Eyes Using Multiple Near-Infrared Camera Sensors
Article Menu
Issue 2 (January-2) cover image

Export Article

Open AccessArticle
Sensors 2019, 19(2), 281; https://doi.org/10.3390/s19020281

Deep RetinaNet-Based Detection and Classification of Road Markings by Visible Light Camera Sensors

Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea
*
Author to whom correspondence should be addressed.
Received: 26 December 2018 / Revised: 7 January 2019 / Accepted: 8 January 2019 / Published: 11 January 2019
(This article belongs to the Special Issue Deep Learning-Based Image Sensors)
  |  
PDF [23278 KB, uploaded 14 January 2019]
  |  

Abstract

Detection and classification of road markings are a prerequisite for operating autonomous vehicles. Although most studies have focused on the detection of road lane markings, the detection and classification of other road markings, such as arrows and bike markings, have not received much attention. Therefore, we propose a detection and classification method for various types of arrow markings and bike markings on the road in various complex environments using a one-stage deep convolutional neural network (CNN), called RetinaNet. We tested the proposed method in complex road scenarios with three open datasets captured by visible light camera sensors, namely the Malaga urban dataset, the Cambridge dataset, and the Daimler dataset on both a desktop computer and an NVIDIA Jetson TX2 embedded system. Experimental results obtained using the three open databases showed that the proposed RetinaNet-based method outperformed other methods for detection and classification of road markings in terms of both accuracy and processing time. View Full-Text
Keywords: detection and classification of road markings; deep CNN; one-stage RetinaNet; NVIDIA Jetson TX2; visible light camera sensor detection and classification of road markings; deep CNN; one-stage RetinaNet; NVIDIA Jetson TX2; visible light camera sensor
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Hoang, T.M.; Nguyen, P.H.; Truong, N.Q.; Lee, Y.W.; Park, K.R. Deep RetinaNet-Based Detection and Classification of Road Markings by Visible Light Camera Sensors. Sensors 2019, 19, 281.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top