IEICE TRANSACTIONS on Information
Obstacle Detection for Unmanned Surface Vehicles by Fusion Refinement Network
Summary :
As a kind of marine vehicles, Unmanned Surface Vehicles (USV) are widely used in military and civilian fields because of their low cost, good concealment, strong mobility and high speed. High-precision detection of obstacles plays an important role in USV autonomous navigation, which ensures its subsequent path planning. In order to further improve obstacle detection performance, we propose an encoder-decoder architecture named Fusion Refinement Network (FRN). The encoder part with a deeper network structure enables it to extract more rich visual features. In particular, a dilated convolution layer is used in the encoder for obtaining a large range of obstacle features in complex marine environment. The decoder part achieves the multiple path feature fusion. Attention Refinement Modules (ARM) are added to optimize features, and a learnable fusion algorithm called Feature Fusion Module (FFM) is used to fuse visual information. Experimental validation results on three different datasets with real marine images show that FRN is superior to state-of-the-art semantic segmentation networks in performance evaluation. And the MIoU and MPA of the FRN can peak at 97.01% and 98.37% respectively. Moreover, FRN could maintain a high accuracy with only 27.67M parameters, which is much smaller than the latest obstacle detection network (WaSR) for USV.
- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.8 pp.1393-1400
- Publication Date
- 2022/08/01
- Publicized
- 2022/05/12
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2021EDP7254
- Type of Manuscript
- PAPER
- Category
- Information Network
Authors
Weina ZHOU
Shanghai Maritime University
Xinxin HUANG
Shanghai Maritime University
Xiaoyang ZENG
Fudan University
Keyword
Latest Issue
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Weina ZHOU, Xinxin HUANG, Xiaoyang ZENG, "Obstacle Detection for Unmanned Surface Vehicles by Fusion Refinement Network" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 8, pp. 1393-1400, August 2022, doi: 10.1587/transinf.2021EDP7254.
Abstract: As a kind of marine vehicles, Unmanned Surface Vehicles (USV) are widely used in military and civilian fields because of their low cost, good concealment, strong mobility and high speed. High-precision detection of obstacles plays an important role in USV autonomous navigation, which ensures its subsequent path planning. In order to further improve obstacle detection performance, we propose an encoder-decoder architecture named Fusion Refinement Network (FRN). The encoder part with a deeper network structure enables it to extract more rich visual features. In particular, a dilated convolution layer is used in the encoder for obtaining a large range of obstacle features in complex marine environment. The decoder part achieves the multiple path feature fusion. Attention Refinement Modules (ARM) are added to optimize features, and a learnable fusion algorithm called Feature Fusion Module (FFM) is used to fuse visual information. Experimental validation results on three different datasets with real marine images show that FRN is superior to state-of-the-art semantic segmentation networks in performance evaluation. And the MIoU and MPA of the FRN can peak at 97.01% and 98.37% respectively. Moreover, FRN could maintain a high accuracy with only 27.67M parameters, which is much smaller than the latest obstacle detection network (WaSR) for USV.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7254/_p
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@ARTICLE{e105-d_8_1393,
author={Weina ZHOU, Xinxin HUANG, Xiaoyang ZENG, },
journal={IEICE TRANSACTIONS on Information},
title={Obstacle Detection for Unmanned Surface Vehicles by Fusion Refinement Network},
year={2022},
volume={E105-D},
number={8},
pages={1393-1400},
abstract={As a kind of marine vehicles, Unmanned Surface Vehicles (USV) are widely used in military and civilian fields because of their low cost, good concealment, strong mobility and high speed. High-precision detection of obstacles plays an important role in USV autonomous navigation, which ensures its subsequent path planning. In order to further improve obstacle detection performance, we propose an encoder-decoder architecture named Fusion Refinement Network (FRN). The encoder part with a deeper network structure enables it to extract more rich visual features. In particular, a dilated convolution layer is used in the encoder for obtaining a large range of obstacle features in complex marine environment. The decoder part achieves the multiple path feature fusion. Attention Refinement Modules (ARM) are added to optimize features, and a learnable fusion algorithm called Feature Fusion Module (FFM) is used to fuse visual information. Experimental validation results on three different datasets with real marine images show that FRN is superior to state-of-the-art semantic segmentation networks in performance evaluation. And the MIoU and MPA of the FRN can peak at 97.01% and 98.37% respectively. Moreover, FRN could maintain a high accuracy with only 27.67M parameters, which is much smaller than the latest obstacle detection network (WaSR) for USV.},
keywords={},
doi={10.1587/transinf.2021EDP7254},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Obstacle Detection for Unmanned Surface Vehicles by Fusion Refinement Network
T2 - IEICE TRANSACTIONS on Information
SP - 1393
EP - 1400
AU - Weina ZHOU
AU - Xinxin HUANG
AU - Xiaoyang ZENG
PY - 2022
DO - 10.1587/transinf.2021EDP7254
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2022
AB - As a kind of marine vehicles, Unmanned Surface Vehicles (USV) are widely used in military and civilian fields because of their low cost, good concealment, strong mobility and high speed. High-precision detection of obstacles plays an important role in USV autonomous navigation, which ensures its subsequent path planning. In order to further improve obstacle detection performance, we propose an encoder-decoder architecture named Fusion Refinement Network (FRN). The encoder part with a deeper network structure enables it to extract more rich visual features. In particular, a dilated convolution layer is used in the encoder for obtaining a large range of obstacle features in complex marine environment. The decoder part achieves the multiple path feature fusion. Attention Refinement Modules (ARM) are added to optimize features, and a learnable fusion algorithm called Feature Fusion Module (FFM) is used to fuse visual information. Experimental validation results on three different datasets with real marine images show that FRN is superior to state-of-the-art semantic segmentation networks in performance evaluation. And the MIoU and MPA of the FRN can peak at 97.01% and 98.37% respectively. Moreover, FRN could maintain a high accuracy with only 27.67M parameters, which is much smaller than the latest obstacle detection network (WaSR) for USV.
ER -
English
