抄録
Lane detection or road detection is one of the key features of autonomous driving. In computer vision area, it is still a very challenging target since there are various types of road scenarios which require a very high robustness of the algorithm. And considering the rather high speed of the vehicles, high efficiency is also a very important requirement for practicable application of autonomous driving. In this paper, we propose a deep convolution neural network based lane detection method, which consider the lane detection task as a pixel level segmentation of the lane markings. We also propose an automatic training data generating method, which can significantly reduce the effort of the training phase. Experiment proves that our method can achieve high accuracy for various road scenes in real-time.
| 元の言語 | English |
|---|---|
| ページ（範囲） | 566-575 |
| ページ数 | 10 |
| ジャーナル | IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences |
| 巻 | E102A |
| 発行部数 | 3 |
| DOI | |
| 出版物ステータス | Published - 2019 3 1 |
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ASJC Scopus subject areas
- Signal Processing
- Computer Graphics and Computer-Aided Design
- Electrical and Electronic Engineering
- Applied Mathematics
これを引用
Fast lane detection based on deep convolutional neural network and automatic training data labeling. / Pan, Xun; Ogai, Harutoshi.
：: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 巻 E102A, 番号 3, 01.03.2019, p. 566-575.研究成果: Article
}
TY - JOUR
T1 - Fast lane detection based on deep convolutional neural network and automatic training data labeling
AU - Pan, Xun
AU - Ogai, Harutoshi
PY - 2019/3/1
Y1 - 2019/3/1
N2 - Lane detection or road detection is one of the key features of autonomous driving. In computer vision area, it is still a very challenging target since there are various types of road scenarios which require a very high robustness of the algorithm. And considering the rather high speed of the vehicles, high efficiency is also a very important requirement for practicable application of autonomous driving. In this paper, we propose a deep convolution neural network based lane detection method, which consider the lane detection task as a pixel level segmentation of the lane markings. We also propose an automatic training data generating method, which can significantly reduce the effort of the training phase. Experiment proves that our method can achieve high accuracy for various road scenes in real-time.
AB - Lane detection or road detection is one of the key features of autonomous driving. In computer vision area, it is still a very challenging target since there are various types of road scenarios which require a very high robustness of the algorithm. And considering the rather high speed of the vehicles, high efficiency is also a very important requirement for practicable application of autonomous driving. In this paper, we propose a deep convolution neural network based lane detection method, which consider the lane detection task as a pixel level segmentation of the lane markings. We also propose an automatic training data generating method, which can significantly reduce the effort of the training phase. Experiment proves that our method can achieve high accuracy for various road scenes in real-time.
KW - Automatic labeling
KW - Deep neural network
KW - Real-time lane detection
UR - http://www.scopus.com/inward/record.url?scp=85064003273&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064003273&partnerID=8YFLogxK
U2 - 10.1587/transfun.E102.A.566
DO - 10.1587/transfun.E102.A.566
M3 - Article
AN - SCOPUS:85064003273
VL - E102A
SP - 566
EP - 575
JO - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
JF - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
SN - 0916-8508
IS - 3
ER -