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Comparative Study of Out-of-the-Box Technology for Automatic Target Detection and Recognition — arXiv:2608.17917

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.17917
License (as archived): CC0-1.0
Archived: 2026-10-18T14:33:45.063Z
SHA-256 of the archived text: c823ebba1effbddf99e7749e6fedcad8b2ac56f4151355e860c30187fb3a2c47
Stable URL of this page: https://metraipolis.com/documento/dc_0e9fc5eaefba

How to cite

Comparative Study of Out-of-the-Box Technology for Automatic Target Detection and Recognition — arXiv:2608.17917 — arXiv (en), https://arxiv.org/abs/2608.17917 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_0e9fc5eaefba (Metraipolis archive dc_0e9fc5eaefba, sha256 c823ebba1effbddf99e7749e6fedcad8b2ac56f4151355e860c30187fb3a2c47)

BibTeX

@misc{dc0e9fc5eaefba,
  title = {Comparative Study of Out-of-the-Box Technology for Automatic Target Detection and Recognition — arXiv:2608.17917},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.17917},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_0e9fc5eaefba; sha256 c823ebba1effbddf99e7749e6fedcad8b2ac56f4151355e860c30187fb3a2c47},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.17917. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Automatic Target Detection and Recognition (ATD/R) is critical for military decision support and (semi-)autonomous operations. Recent advances in object detection and artificial intelligence (AI) significantly boosted the potential performance of ATD/R. However, the scarcity of publicly available military datasets limits the application of these systems. As a solution, this paper explores the use of publicly available models and civilian datasets to achieve reasonable performance in military contexts. We benchmark several state-of-the-art models, including six iterations of the YOLO series and two variations on the DETR framework, on a newly acquired military relevant dataset. This dataset features military vehicles and challenging circumstances, including various degrees of occlusions and small targets. The out-of-the-box version of each model is validated alongside a version finetuned on the VisDrone dataset. This dataset features small objects, an Air-to-Ground (A2G) perspective and relevant classes, potentially generalizing to our military ATD/R task. We compare the performance of the models using [email protected] and [email protected]:0.95, across A2G and Ground-to-Ground (G2G) perspective, target size and model size, giving insight into the real-time capabilities of models. Our main findings are: (1) bigger models outperform smaller models, (2) DETR-based models show promising results compared to the YOLO series,(3) fine-tuning models on an out-of-domain A2G dataset, improves their A2G performance and slightly improves their performance on small objects, but (4) all models still struggle with detecting small objects in an A2G scenario. We conclude that, despite recent advances in object detection, in-domain training is still crucial for creating capable ATD/R systems.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-19 in cs.AI).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.17917 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T14:27:54.467Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-19?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).