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    15827188114

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    测绘遥感信息工程全国重点实验室

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    1976-03-21

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    武汉大学

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    测绘遥感信息工程全国重点实验室

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    2004-07-01

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    武汉大学星湖实验大楼713

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Paper Publications

Current position: Home > Scientific Research > Paper Publications

TripleA: An Unsupervised Domain Adaptation Framework for Nighttime VRU Detection

  • Time:2025-06-01
  • Hits:

  • DOI number:

    10.1109/tits.2025.3548804

  • Affiliation of Author(s):

    Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China

  • Journal:

    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

  • Funded by:

    This work was supported in part by Shanxi Provincial Science and Technology Major Special Project un

  • Key Words:

    Lighting,Noise,Degradation,Noise reduction,Training,Image enhancement,Annotations,Detectors,Atmospheric modeling,Roads,Vulnerable road user detection,unsupervised domain adaptation,image enhancement,low-light,denoising

  • Abstract:

    Detecting vulnerable road users (VRUs) at night presents significant challenges. Numerous methods rely heavily on annotations, yet the low visibility of nighttime images poses difficulties for labeling. To obviate the need for nighttime annotations, unsupervised domain adaptation manifests as a viable solution. However, existing approaches primarily focus on semantic-level domain gaps, often overlooking pixel-level discrepancies caused by inherent degradations in the nighttime domain. These degradations can impair machine vision and limit detection performance. In this paper, we propose Triple

  • Co-author:

    Cheng, Yulin,Ding, Yu, Jiaming, Gui,Wang,Wang

  • Indexed by:

    Journal paper

  • Correspondence Author:

    Zhenfeng,Shao

  • Document Type:

    Article

  • Volume:

    26

  • Issue:

    6

  • Page Number:

    8320-8336

  • ISSN No.:

    1524-9050

  • Translation or Not:

    no

  • Date of Publication:

    2025-06-01

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