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

作　　者：Fei WANG Wanyu LI Miao LIU Jingchun ZHOU Weishi ZHANG 

机构地区：[1]College of Information Science and Technology,Dalian Maritime University,Dalian 116026,China [2]College of Transportation Engineering,Dalian Maritime University,Dalian 116026,China

出　　处：《Frontiers of Information Technology & Electronic Engineering》2023年第6期828-843,共16页信息与电子工程前沿（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.62103072);the Postdoctoral Science Foundation of China(No.2021M690502)。

摘　　要：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.

关 键 词：Underwater object detection Sonar data representation Feature fusion 

分 类 号：TN911.73[电子电信—通信与信息系统] TP391.41[电子电信—信息与通信工程]