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基于深度学习的水下图像目标检测综述

罗逸豪 刘奇佩 张吟 周河宇 张钧陶 曹翔

罗逸豪, 刘奇佩, 张吟, 周河宇, 张钧陶, 曹翔. 基于深度学习的水下图像目标检测综述[J]. 电子与信息学报, 2023, 45(10): 3468-3482. doi: 10.11999/JEIT221402
引用本文: 罗逸豪, 刘奇佩, 张吟, 周河宇, 张钧陶, 曹翔. 基于深度学习的水下图像目标检测综述[J]. 电子与信息学报, 2023, 45(10): 3468-3482. doi: 10.11999/JEIT221402
LUO Yihao, LIU Qipei, ZHANG Yin, ZHOU Heyu, ZHANG Juntao, CAO Xiang. Review of Underwater Image Object Detection Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3468-3482. doi: 10.11999/JEIT221402
Citation: LUO Yihao, LIU Qipei, ZHANG Yin, ZHOU Heyu, ZHANG Juntao, CAO Xiang. Review of Underwater Image Object Detection Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3468-3482. doi: 10.11999/JEIT221402

基于深度学习的水下图像目标检测综述

doi: 10.11999/JEIT221402
详细信息
    作者简介:

    罗逸豪:男,博士,研究方向为深度学习、计算机视觉、声呐图像处理

    刘奇佩:男,博士,研究方向为水声信号处理、声呐图像处理

    张吟:男,高级工程师,研究方向为声呐图像处理

    周河宇:男,博士,研究方向为深度学习、计算机视觉

    张钧陶:男,工程师,研究方向为深度学习、计算机视觉

    曹翔:男,博士,研究方向为深度学习、计算机视觉

    通讯作者:

    罗逸豪 luoyihao@hust.edu.cn

  • 中图分类号: TN911.73; TP391.4

Review of Underwater Image Object Detection Based on Deep Learning

  • 摘要: 水下图像目标检测是水下智能化探测的核心技术之一,广泛应用于工业及军事领域。深度学习相关技术的突破为水下图像目标检测的发展带来了新的机遇,但是目前该领域的综述较为陈旧,并且缺乏一定的系统性和全面性。该文对基于深度学习的水下可见光图像和声呐图像目标检测研究工作进行了详细总结与分析。首先,对基于深度学习的通用目标检测算法框架进行了梳理,包含骨干网络、颈部模块、检测头部、训练算法、推理策略、数据集6项要素,并系统性地总结了每个要素存在的问题及最新研究工作;然后,调研了水下可见光图像目标检测最新进展,分别从数据集发展、模型设计、训练算法进行总结;同时,归纳并分析了水下声呐图像目标检测相关工作,包含前视、侧扫、合成孔径3种声呐。最后,结合深度学习最新研究探讨了该领域的研究趋势。
  • 图  1  基于深度学习的目标检测模型

    表  1  可用于水下可见光图像目标检测的数据集

    数据集训练集图像数测试集图像数类别数类别描述用途年份
    URPC2017[53]17655985*3海参、海胆、扇贝目标检测2017
    URPC2018[53]2901800*4海参、海胆、扇贝、海星目标检测2018
    URPC2019[53]47571029*4海参、海胆、扇贝、海星目标检测2019
    URPC2020-ZJ[53]55432000*4海参、海胆、扇贝、海星目标检测2020
    URPC2020-DL[53]65752400*4海参、海胆、扇贝、海星目标检测2020
    URPC2021[53]76002400*4海参、海胆、扇贝、海星目标检测2021
    RUIE-UHTS[54]3003海参、海胆、扇贝目标检测2020
    UDD[55]18274003海参、海胆、扇贝目标检测2022
    UWD[56]100004海参、海胆、扇贝、海星目标检测2020
    DUO[57]667111114海参、海胆、扇贝、海星目标检测2021
    Fish4Knowledge[58]2737023海底鱼类目标检测2013
    Brackish[59]145186大鱼、小鱼、水母、螃蟹等目标检测2019
    Marine Litter[60]57203塑料垃圾、人为目标、生物目标检测2019
    TrashCan[61]721222海底垃圾、动植物等目标检测/分割2020
    SUIM[62]15251108鱼类、珊瑚、植物、人、残骸等目标检测/分割2020
    Kyutech10K[63]107287虾、鱿鱼、螃蟹、鲨鱼等图像分类2018
    UIEB[64]9508各类珊瑚与海洋生物等图像增强2020
    MUED[65]8600430430个海底物体显著性检测2019
    UOT32[66]242413232个海底目标视频目标跟踪2019
    UOT100[67]74042104104个海底目标视频目标跟踪2021
    下载: 导出CSV
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  • 收稿日期:  2022-11-08
  • 修回日期:  2023-04-10
  • 网络出版日期:  2023-04-24
  • 刊出日期:  2023-10-31

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