Name
Title | ||
|---|---|---|
Fast Lane Detection Based On Deep Convolutional Neural Network And Automatic Training Data Labeling |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1587/transfun.E102.A.566 | IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES |
Keywords | Field | DocType |
real-time lane detection, deep neural network, automatic labeling | Training set,Pattern recognition,Convolutional neural network,Theoretical computer science,Lane detection,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
E102A | 3 | 0916-8508 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xun Pan | 1 | 4 | 2.46 |
| Harutoshi Ogai | 2 | 13 | 9.93 |