Title
Inter-Level Feature Balanced Fusion Network for Street Scene Segmentation
Abstract
Semantic segmentation, as a pixel-level recognition task, has been widely used in a variety of practical scenes. Most of the existing methods try to improve the performance of the network by fusing the information of high and low layers. This kind of simple concatenation or element-wise addition will lead to the problem of unbalanced fusion and low utilization of inter-level features. To solve this problem, we propose the Inter-Level Feature Balanced Fusion Network (IFBFNet) to guide the inter-level feature fusion towards a more balanced and effective direction. Our overall network architecture is based on the encoder-decoder architecture. In the encoder, we use a relatively deep convolution network to extract rich semantic information. In the decoder, skip-connections are added to connect and fuse low-level spatial features to restore a clearer boundary expression gradually. We add an inter-level feature balanced fusion module to each skip connection. Additionally, to better capture the boundary information, we added a shallower spatial information stream to supplement more spatial information details. Experiments have proved the effectiveness of our module. Our IFBFNet achieved a competitive performance on the Cityscapes dataset with only finely annotated data used for training and has been greatly improved on the baseline network.
Year
DOI
Venue
2021
10.3390/s21237844
SENSORS
Keywords
DocType
Volume
semantic segmentation, encoder-decoder, feature balanced fusion, Cityscapes
Journal
21
Issue
ISSN
Citations 
23
1424-8220
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Dongqian Li100.68
Cien Fan213.06
Lian Zou312.38
Qi Zuo400.68
Hao Jiang501.01
Yifeng Liu603.72