| Autor: |
Artacho B; Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA., Savakis A; Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA. |
| Jazyk: |
angličtina |
| Zdroj: |
Sensors (Basel, Switzerland) [Sensors (Basel)] 2019 Dec 05; Vol. 19 (24). Date of Electronic Publication: 2019 Dec 05. |
| DOI: |
10.3390/s19245361 |
| Abstrakt: |
We propose a new efficient architecture for semantic segmentation, based on a "Waterfall" Atrous Spatial Pooling architecture, that achieves a considerable accuracy increase while decreasing the number of network parameters and memory footprint. The proposed Waterfall architecture leverages the efficiency of progressive filtering in the cascade architecture while maintaining multiscale fields-of-view comparable to spatial pyramid configurations. Additionally, our method does not rely on a postprocessing stage with Conditional Random Fields, which further reduces complexity and required training time. We demonstrate that the Waterfall approach with a ResNet backbone is a robust and efficient architecture for semantic segmentation obtaining state-of-the-art results with significant reduction in the number of parameters for the Pascal VOC dataset and the Cityscapes dataset. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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