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Fei WANG, Wanyu LI, Miao LIU, Jingchun ZHOU, Weishi ZHANG. Underwater object detection by fusing features from different representations of sonar data[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Underwater object detection by fusing features from different representations of sonar data",
author="Fei WANG, Wanyu LI, Miao LIU, Jingchun ZHOU, Weishi ZHANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200429"
}
%0 Journal Article
%T Underwater object detection by fusing features from different representations of sonar data
%A Fei WANG
%A Wanyu LI
%A Miao LIU
%A Jingchun ZHOU
%A Weishi ZHANG
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
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%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200429
TY - JOUR
T1 - Underwater object detection by fusing features from different representations of sonar data
A1 - Fei WANG
A1 - Wanyu LI
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A1 - Jingchun ZHOU
A1 - Weishi ZHANG
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
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EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2200429
Abstract: Modern underwater object detection methods recognize objects from sonar data based on their geometric shapes. However, the distortion of objects during data acquisition and representation is seldom considered. In this paper, we present a detailed summary of representations for sonar data and a concrete analysis of the geometric characteristics of different data representations. Based on this, a feature fusion framework is proposed to fully utilize the intensity features extracted from the polar image representation and the geometric features learned from the point cloud representation of sonar data. Three feature fusion strategies are presented to investigate the impact of feature fusion on different components of the detection pipeline. In addition, the fusion strategies can be easily integrated into other detectors, such as the YOLO series. The effectiveness of our proposed framework and feature fusion strategies are demonstrated on a public sonar dataset captured in real-world underwater environments. The experimental results show that our method benefits both the region proposal and the object classification modules in the detectors.
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