We propose a novel modular CNN architecture that provides semantic segmentation and understanding of outdoor street environment images. Our solution processes a 512x1024 resolution image on a single Titan Xp GPU at 37.4 FPS attaining 70.4% IoU on the Cityscapes test dataset.
Mazzini, D., Buzzelli, M., Pau, D., Schettini, R. (2018). A CNN architecture for efficient semantic segmentation of street scenes. In Proceedings 2018 IEEE 8th International Conference on Consumer Electronics-Berlin (ICCE-Berlin) (pp.1-6). IEEE Computer Society [10.1109/ICCE-Berlin.2018.8576193].
A CNN architecture for efficient semantic segmentation of street scenes
Mazzini, Davide
;Buzzelli, Marco;Schettini, Raimondo
2018
Abstract
We propose a novel modular CNN architecture that provides semantic segmentation and understanding of outdoor street environment images. Our solution processes a 512x1024 resolution image on a single Titan Xp GPU at 37.4 FPS attaining 70.4% IoU on the Cityscapes test dataset.
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