{
  "abstract": "Infrared object detection plays an important role in low-light and complex-environment perception. However, infrared images often exhibit low signal-to-noise ratio, weak target-background contrast, and limited texture information, which can degrade detection performance in small-target and cluttered-background scenarios while making it difficult to balance detection accuracy and model complexity. To address these challenges, this paper proposes TCLNet, a Thermal Contrast-Aware Lightweight Network for infrared object detection. TCLNet introduces a Thermal Contrast Encoder (TCE) to enhance local contrast-sensitive target representations and a Thermal-Guided Cross-Feature Attention (TGCA) mechanism to promote interactions between contrast-enhanced and semantic features, thereby improving target discrimination and suppressing background interference.Experiments were conducted independently on the M3FD, FLIR ADAS v2 (denoted as FLIR_2), and NUDT-SIRST1 datasets. TCLNet achieves mAP50 values of 60.5%, 45.4%, and 91.4%, respectively, corresponding to improvements of 12.8, 11.3, and 7.5 percentage points over the YOLO12n baseline. Meanwhile, the model maintains a compact size of approximately 2M parameters. These results demonstrate that TCLNet improves infrared target detection, particularly in low-SNR and small-target conditions, while achieving a favorable balance between detection accuracy and model complexity.",
  "abstract_shown": true,
  "affiliations": [
    "Xijing University, Xi'an, Shaanxi, China."
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
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        0
      ],
      "name": "Xiyuan Zhu",
      "orcid": "0009-0008-1723-9284"
    },
    {
      "affs": [
        0
      ],
      "name": "Hongtao Dang"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaoyuan Jin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9wdpcw1rf7b52.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0361114",
  "fulltext_url": "https://europepmc.org/articles/PMC13690208",
  "id": "9wdpcw1rf7b52",
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  "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": [
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      "openalex",
      "2202|Infrared Target Detection Methodologies"
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  "pdf_url": "https://europepmc.org/articles/PMC13690208?pdf=render",
  "pmcid": "PMC13690208",
  "pmid": "42911949",
  "published": "2026-10-22",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/9wdpcw1rf7b52.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:23Z",
      "native_id": "10.1371/journal.pone.0361114",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0361114"
    },
    {
      "first_seen": "2026-10-30T12:20:10Z",
      "native_id": "MED:42911949",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42911949"
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  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
    }
  ],
  "title": "TCLNet: A Thermal Contrast-Aware Lightweight Network for infrared object detection",
  "title_text": "TCLNet: A Thermal Contrast-Aware Lightweight Network for infrared object detection",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:10Z",
  "url": "https://research.draconitic.com/paper/9wdpcw1rf7b52",
  "venue": "PLOS One",
  "versions": []
}