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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2200429


Underwater object detection by fusing features from different representations of sonar data


Author(s):  Fei WANG, Wanyu LI, Miao LIU, Jingchun ZHOU, Weishi ZHANG

Affiliation(s):  College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China; more

Corresponding email(s):   feiwang@dlmu.edu.cn, zhoujingchun@dlmu.edu.cn, teesiv@dlmu.edu.cn

Key Words:  Underwater object detection, Sonar data representation, Feature fusion


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): .

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publisher="Zhejiang University Press & Springer",
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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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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