Deep Road Scene Understanding

Road scene understanding is a difficult task in autonomous driving. In this letter, we propose a novel deep encoder-decoder architecture for road scene understanding in an end-to-end manner. This core trainable understanding engine includes an encoder network, a decoder network with two streams, and...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE signal processing letters Jg. 26; H. 4; S. 587 - 591
Hauptverfasser: Zhou, Wujie, Lv, Sijia, Jiang, Qiuping, Yu, Lu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2019
Schlagworte:
Computer architecture
Convolution
Decoding
encoder–decoder architecture
Feature extraction
Fuses
fusion layer
Road scene understanding
Roads
Semantics
skip connection
ISSN:1070-9908, 1558-2361
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Road scene understanding is a difficult task in autonomous driving. In this letter, we propose a novel deep encoder-decoder architecture for road scene understanding in an end-to-end manner. This core trainable understanding engine includes an encoder network, a decoder network with two streams, and a pixel-level fusion network with classification layer. The encoder network is composed of the front-end model of the classical convolution neural network, VGGNet. The decoder network with two streams includes multi-scale skip connection modules to reduce the down-scaling effect. Finally, a fusion network fuses the two-level information from the two streams of the decoder network for precise pixel-level classification. Additionally, the convolution layer is added to each skip connection module to increase the depth of the architecture. Our architecture achieves outstanding performance on the publicly available CamVid dataset and significantly outperforms previous architectures. This deep architecture is ideal for road scene understanding.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2019.2896793