{
  "abstract": "Object detection has evolved dramatically over three decades, transitioning from handcrafted features to deep learning, transformers, and now large language models. This paper provides a comprehensive systematic review of this evolution, covering traditional methods (SIFT, HOG, Viola-Jones), CNN-based single- and two-stage detectors (R-CNN family, YOLO v1-v12, SSD), transformer-based architectures (DETR, ViT, Swin), and emerging LLM-driven approaches (GPT-4V, Grounding DINO, YOLO-World). We benchmark and compare these methods using key metrics-mAP, IoU, FPS-and analyze over 50 datasets across generic, autonomous driving, medical, remote sensing, and agricultural domains. Our critical synthesis identifies persistent research gaps: lightweight edge deployment, sensor fusion for real-time systems, bias and fairness in AI, and multimodal vision-language integration. By consolidating three decades of progress and outlining future challenges, this survey serves as a valuable resource for researchers and practitioners seeking to develop accurate, efficient, and reliable object detection systems.",
  "abstract_shown": true,
  "affiliations": [
    "School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.",
    "Department of Engineering & Technology, Trine University, Angola, IN 46703, USA.",
    "International Project Execution and Service Center, Qingdao TGOOD Electric Co., Ltd., Qingdao 266104, China."
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Md Faishal Rahaman",
      "orcid": "0000-0001-5656-1542"
    },
    {
      "affs": [
        0
      ],
      "name": "Xueyuan Li",
      "orcid": "0000-0003-1112-5610"
    },
    {
      "affs": [
        1
      ],
      "name": "Md Nagib Mahfuz Sunny",
      "orcid": "0009-0007-2875-1147"
    },
    {
      "affs": [
        0
      ],
      "name": "Muhammad Amjad",
      "orcid": "0009-0008-0068-1439"
    },
    {
      "affs": [
        0
      ],
      "name": "Xin Gao",
      "orcid": "0000-0002-7317-8059"
    },
    {
      "affs": [
        2
      ],
      "name": "Sakib Hasan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4vs0h6fc0dtj1.bib",
  "cited_by": 0,
  "doi": "10.3390/s26206663",
  "fulltext_url": "https://europepmc.org/articles/PMC13709326",
  "id": "4vs0h6fc0dtj1",
  "issns": [
    "1424-8220"
  ],
  "landing_url": "https://europepmc.org/article/MED/42944526",
  "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": [
    [
      "openalex",
      "1707|Advanced Neural Network Applications"
    ]
  ],
  "openalex": "W7204770977",
  "pdf_url": "https://europepmc.org/articles/PMC13709326?pdf=render",
  "pmcid": "PMC13709326",
  "pmid": "42944526",
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4vs0h6fc0dtj1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:12Z",
      "native_id": "MED:42944526",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42944526"
    }
  ],
  "subjects": [],
  "title": "Object Detection in Computer Vision: A Comprehensive Review of Algorithms, Datasets, and Evaluation Techniques.",
  "title_text": "Object Detection in Computer Vision: A Comprehensive Review of Algorithms, Datasets, and Evaluation Techniques.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:12Z",
  "url": "https://research.draconitic.com/paper/4vs0h6fc0dtj1",
  "venue": "Sensors (Basel, Switzerland)",
  "versions": []
}