{
  "abstract": "Nighttime unmanned aerial vehicle object detection is challenged by degraded visible-light textures, cluttered infrared thermal responses, and local spatial discrepancies between infrared and visible images. To address these issues, we propose IV-PAFNet, an infrared-visible purification, alignment, and fusion network designed for object detection in nighttime and low-visibility conditions. IV-PAFNet consists of two primary components. The infrared signal purification and spatial alignment module applies learnable high-pass filtering and gated residual fusion to refine infrared responses, followed by visible-guided offset prediction and differentiable resampling to alleviate local cross-modal spatial discrepancies. The multi-scale fusion attention enhancement module performs channel-wise modality recalibration and spatial reliability weighting at multiple feature-pyramid levels, allowing the network to adapt the contribution of each modality to local imaging conditions. In the training-based DroneVehicle and generalization-based Camera-vehicle1 datasets, IV-PAFNet achieves 62.81% mAP@0.5:0.95 and 51.4% mAP@0.5:0.95, respectively. Additional controlled experiments investigate the distinct roles of infrared purification, visible-guided alignment, channel recalibration, and spatial reliability modeling, while statistical and computational analyses characterize run-to-run variation and inference cost. The code is available at https://github.com/iv-pafnet/IV-PAFNet.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer Science and Technology, Shandong University of Technology, Zibo, Shandong, China."
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Cheng Yuan"
    },
    {
      "affs": [
        0
      ],
      "name": "Shichang Shang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jinghao Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Yang Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Bin Zhou",
      "orcid": "0000-0002-0314-2595"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a0d3pg3xqf4r2.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0361401",
  "fulltext_url": "https://europepmc.org/articles/PMC13716215",
  "id": "a0d3pg3xqf4r2",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0361401",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Algorithms"
    ],
    [
      "heading",
      "Applied Mathematics"
    ],
    [
      "heading",
      "Artificial Intelligence"
    ],
    [
      "heading",
      "Bioinformatics"
    ],
    [
      "heading",
      "Biology and Life Sciences"
    ],
    [
      "heading",
      "Classical Mechanics"
    ],
    [
      "heading",
      "Cognitive Psychology"
    ],
    [
      "heading",
      "Cognitive Science"
    ],
    [
      "heading",
      "Computer and Information Sciences"
    ],
    [
      "heading",
      "Convolution"
    ],
    [
      "heading",
      "Database and Informatics Methods"
    ],
    [
      "heading",
      "Engineering and Technology"
    ],
    [
      "heading",
      "Machine Learning"
    ],
    [
      "heading",
      "Machine Learning Algorithms"
    ],
    [
      "heading",
      "Mathematical Functions"
    ],
    [
      "heading",
      "Mathematical and Statistical Techniques"
    ],
    [
      "heading",
      "Mathematics"
    ],
    [
      "heading",
      "Multilayer Perceptrons"
    ],
    [
      "heading",
      "Neuroscience"
    ],
    [
      "heading",
      "Perception"
    ],
    [
      "heading",
      "Perceptrons"
    ],
    [
      "heading",
      "Physical Sciences"
    ],
    [
      "heading",
      "Physics"
    ],
    [
      "heading",
      "Preprocessing"
    ],
    [
      "heading",
      "Psychology"
    ],
    [
      "heading",
      "Research Article"
    ],
    [
      "heading",
      "Research and Analysis Methods"
    ],
    [
      "heading",
      "Sensory Perception"
    ],
    [
      "heading",
      "Sequence Alignment"
    ],
    [
      "heading",
      "Sequence Analysis"
    ],
    [
      "heading",
      "Signal Filtering"
    ],
    [
      "heading",
      "Signal Processing"
    ],
    [
      "heading",
      "Simulation and Modeling"
    ],
    [
      "heading",
      "Social Sciences"
    ],
    [
      "heading",
      "Software Engineering"
    ],
    [
      "heading",
      "Vibration"
    ],
    [
      "heading",
      "Vision"
    ],
    [
      "openalex",
      "1707|Advanced Neural Network Applications"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Perception/Sensory perception"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Perception/Sensory perception/Vision"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Sensory perception"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Sensory perception/Vision"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Perception/Sensory perception"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Perception/Sensory perception/Vision"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Machine learning algorithms/Perceptrons/Multilayer perceptrons"
    ],
    [
      "plos",
      "/Computer and information sciences/Software engineering/Preprocessing"
    ],
    [
      "plos",
      "/Engineering and technology/Signal processing/Signal filtering"
    ],
    [
      "plos",
      "/Engineering and technology/Software engineering/Preprocessing"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Applied mathematics/Algorithms/Machine learning algorithms/Perceptrons/Multilayer perceptrons"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Vibration"
    ],
    [
      "plos",
      "/Research and analysis methods/Database and informatics methods/Bioinformatics/Sequence analysis/Sequence alignment"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Mathematical functions/Convolution"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms/Machine learning algorithms/Perceptrons/Multilayer perceptrons"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Perception/Sensory perception"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Perception/Sensory perception/Vision"
    ]
  ],
  "openalex": "W7224096930",
  "pdf_url": "https://europepmc.org/articles/PMC13716215?pdf=render",
  "pmcid": "PMC13716215",
  "pmid": "42949925",
  "published": "2026-10-28",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/a0d3pg3xqf4r2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.1371/journal.pone.0361401",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0361401"
    },
    {
      "first_seen": "2026-10-31T00:20:33Z",
      "native_id": "MED:42949925",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42949925"
    },
    {
      "first_seen": "2026-11-01T12:23:15Z",
      "native_id": "PMC13716215",
      "source": "pmc",
      "url": "https://doi.org/10.1371/journal.pone.0361401"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "heading:Bioinformatics",
      "subfield": "bioinformatics"
    },
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    },
    {
      "field": "mathematics",
      "origin": "heading:Mathematics",
      "subfield": "general"
    },
    {
      "field": "physics",
      "origin": "heading:Physics",
      "subfield": "general"
    }
  ],
  "title": "IV-PAFNet: Infrared-visible cross-modal alignment and fusion network for nighttime UAV object detection",
  "title_text": "IV-PAFNet: Infrared-visible cross-modal alignment and fusion network for nighttime UAV object detection",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:23:15Z",
  "url": "https://research.draconitic.com/paper/a0d3pg3xqf4r2",
  "venue": "PLOS One",
  "versions": []
}