IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2019-10-18 15:05
Study of Recognizing Road Surface Conditions using Deep Learning Applied for RGBD images Obtained from an Environmental Monitoring Robot -- Comparative Studies of SegNet-Basic and ENet as well as Height and Surface Curvature Features --
Takuya Hayashi, Takeo Kaneko, Junya Morimoto (Waseda Univ.), Junji Yamato (Kogakuin Univ.), Hiroyuki Ishii, Jun Ohya, Atsuo Takanishi (Waseda Univ.) PRMU2019-39
Abstract (in Japanese) (See Japanese page) 
(in English) An environmental monitoring robot that moves safely and autonomously needs a function to recognize the state of the ground area. This paper presents a novel two-stage approach for recognizing road surface condition from RGBD image sensed using Kinect v2. The first stage, based on convolutional neural network (CNN) classifier, lists the candidates for the obstruction class and road surface class label (attribute) information for each pixel from the RGB information. The second stage, a determinator using 3-D point clouds information (height or surface curvature features), generates an estimated label image by determining whether it is an obstacle or a road surface class. In this paper, we evaluated SegNet-Basic and ENet as a comparison of the CNN models used in the approach. In the evaluation, we used data collected for the actual natural environment. Experimental results show the effectiveness of the action determination for autonomous search based on road surface recognition using ENet network model and height information obtained from point cloud information.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep Neural Network / SegNet-basic / ENet / 3-D Point Clouds / Recognizing Road Surface Conditions / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 235, PRMU2019-39, pp. 41-46, Oct. 2019.
Paper # PRMU2019-39 
Date of Issue 2019-10-11 (PRMU) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF PRMU2019-39

Conference Information
Committee PRMU  
Conference Date 2019-10-18 - 2019-10-19 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2019-10-PRMU 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study of Recognizing Road Surface Conditions using Deep Learning Applied for RGBD images Obtained from an Environmental Monitoring Robot 
Sub Title (in English) Comparative Studies of SegNet-Basic and ENet as well as Height and Surface Curvature Features 
Keyword(1) Deep Neural Network  
Keyword(2) SegNet-basic  
Keyword(3) ENet  
Keyword(4) 3-D Point Clouds  
Keyword(5) Recognizing Road Surface Conditions  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Takuya Hayashi  
1st Author's Affiliation Waseda University (Waseda Univ.)
2nd Author's Name Takeo Kaneko  
2nd Author's Affiliation Waseda University (Waseda Univ.)
3rd Author's Name Junya Morimoto  
3rd Author's Affiliation Waseda University (Waseda Univ.)
4th Author's Name Junji Yamato  
4th Author's Affiliation Kogakuin University (Kogakuin Univ.)
5th Author's Name Hiroyuki Ishii  
5th Author's Affiliation Waseda University (Waseda Univ.)
6th Author's Name Jun Ohya  
6th Author's Affiliation Waseda University (Waseda Univ.)
7th Author's Name Atsuo Takanishi  
7th Author's Affiliation Waseda University (Waseda Univ.)
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
Speaker Author-1 
Date Time 2019-10-18 15:05:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2019-39 
Volume (vol) vol.119 
Number (no) no.235 
Page pp.41-46 
#Pages
Date of Issue 2019-10-11 (PRMU) 


[Return to Top Page]

[Return to IEICE Web Page]


The Institute of Electronics, Information and Communication Engineers (IEICE), Japan