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Small Object Augmentation of Urban Scenes for Real-Time Semantic Segmentation.
- Source :
- IEEE Transactions on Image Processing; 2020, Vol. 29, p5175-5190, 16p
- Publication Year :
- 2020
-
Abstract
- Semantic segmentation is a key step in scene understanding for autonomous driving. Although deep learning has significantly improved the segmentation accuracy, current high-quality models such as PSPNet and DeepLabV3 are inefficient given their complex architectures and reliance on multi-scale inputs. Thus, it is difficult to apply them to real-time or practical applications. On the other hand, existing real-time methods cannot yet produce satisfactory results on small objects such as traffic lights, which are imperative to safe autonomous driving. In this paper, we improve the performance of real-time semantic segmentation from two perspectives, methodology and data. Specifically, we propose a real-time segmentation model coined Narrow Deep Network (NDNet) and build a synthetic dataset by inserting additional small objects into the training images. The proposed method achieves 65.7% mean intersection over union (mIoU) on the Cityscapes test set with only 8.4G floating-point operations (FLOPs) on $1024\times 2048$ inputs. Furthermore, by re-training the existing PSPNet and DeepLabV3 models on our synthetic dataset, we obtained an average 2% mIoU improvement on small objects. [ABSTRACT FROM AUTHOR]
- Subjects :
- DEEP learning
TRAFFIC signs & signals
AUTONOMOUS vehicles
TRAFFIC safety
Subjects
Details
- Language :
- English
- ISSN :
- 10577149
- Volume :
- 29
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Image Processing
- Publication Type :
- Academic Journal
- Accession number :
- 170078326
- Full Text :
- https://doi.org/10.1109/TIP.2020.2976856