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Erschienen in:
The Effects of Edutainment Strategies on Student Motivation and Engagement in College EFL Classes Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

DPNet: A Dual Path Network for Road Scene Semantic Segmentation

verfasst von : Lu Ye, Jiayi Zhu, Wujie Zhou, Ting Duan, Sugianto Sugianto, George Kofi Agordzo, Derrick Yeboah, Mukonde Tonderayi Kevin

Erschienen in: Transactions on Edutainment XVI

Verlag: Springer Berlin Heidelberg

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Abstract

Road scene segmentation has always been regarded as a pixel-wise task in computer vision studies. In this paper, we introduce a practical and new features fusion structure named “Dual Path Network” for road semantic segmentation. This form aims to reduce the gap between low-level and high-level information, thereby improving features fusion. The Dual Path consists of two subpaths: Context Path and Spatial Path. In the Context Path, we select a pre-trained ResNet-101 model as the backbone and use multi-scale convolution blocks comprise the Spatial Path. Then, we create a fusion residual block and channel attention model to further optimize the network. The results of the experiment confirm a state-of-the-art mean intersection-over-union of 68.5% using the CamVid dataset.

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Vorheriges Kapitel An Annotation Method for Artwork Attributes Based on Visual Perception
Nächstes Kapitel Detecting Aging Substation Transformers by Audio Signal with Deep Neural Network
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Metadaten
Titel
DPNet: A Dual Path Network for Road Scene Semantic Segmentation
verfasst von
Lu Ye
Jiayi Zhu
Wujie Zhou
Ting Duan
Sugianto Sugianto
George Kofi Agordzo
Derrick Yeboah
Mukonde Tonderayi Kevin
Copyright-Jahr
2020
Verlag
Springer Berlin Heidelberg
Buch
Transactions on Edutainment XVI

Print ISBN: 978-3-662-61509-6

Electronic ISBN: 978-3-662-61510-2

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-662-61510-2_6