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CLC number: TN911.73; TP391.41
On-line Access: 2023-07-03
Received: 2022-10-03
Revision Accepted: 2023-02-02
Crosschecked: 2023-07-03
Cited: 0
Clicked: 602
Citations: Bibtex RefMan EndNote GB/T7714
https://orcid.org/0000-0003-0519-8397
Underwater object detection by fusing features from different representations of sonar data
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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, 2023, 24(6): 828-843.
@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="24",
number="6",
pages="828-843",
year="2023",
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 Frontiers of Information Technology & Electronic Engineering
%V 24
%N 6
%P 828-843
%@ 2095-9184
%D 2023
%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
A1 - Miao LIU
A1 - Jingchun ZHOU
A1 - Weishi ZHANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 6
SP - 828
EP - 843
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
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 use 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 You Only Look Once (YOLO) series. The effectiveness of our proposed framework and feature fusion strategies is demonstrated on a public sonar dataset captured in real-world underwater environments. Experimental results show that our method benefits both the region proposal and the object classification modules in the detectors.
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