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Uncertainty-Aware Multimodal Anti-UAV Detection via Evidential Fusion and Conflict-Discounted Belief Aggregation — arXiv:2608.29235

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.29235
License (as archived): CC0-1.0
Archived: 2026-10-17T09:53:45.488Z
SHA-256 of the archived text: fa926f013711d9acc67698e5b888b4708f54a702a7624082653d5d397d86983a
Stable URL of this page: https://metraipolis.com/documento/dc_170ea201f72e

How to cite

Uncertainty-Aware Multimodal Anti-UAV Detection via Evidential Fusion and Conflict-Discounted Belief Aggregation — arXiv:2608.29235 — arXiv (en), https://arxiv.org/abs/2608.29235 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_170ea201f72e (Metraipolis archive dc_170ea201f72e, sha256 fa926f013711d9acc67698e5b888b4708f54a702a7624082653d5d397d86983a)

BibTeX

@misc{dc170ea201f72e,
  title = {Uncertainty-Aware Multimodal Anti-UAV Detection via Evidential Fusion and Conflict-Discounted Belief Aggregation — arXiv:2608.29235},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.29235},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_170ea201f72e; sha256 fa926f013711d9acc67698e5b888b4708f54a702a7624082653d5d397d86983a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.29235. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Anti-UAV perception systems must remain reliable when sensor streams degrade under occlusion, fast motion, or modality-specific failure. Existing multimodal anti-UAV systems fuse RGB and thermal streams deterministically, without modeling predictive uncertainty, and cannot express doubt when streams disagree. Evidential Deep Learning (EDL) produces calibrated per-class uncertainty in a single forward pass. EDTC already exploits this for thermal-only perception, yet cross-modal evidential fusion remains unaddressed. This paper extends EDTC to multimodal RGB-Thermal perception via Discounted Belief Fusion (DBF), which converts inter-modal conflict into uncertainty mass before aggregating stream opinions. Bounding boxes are resolved by selecting the lower-uncertainty modality. On the Anti-UAV benchmark, multimodal fusion consistently outperforms either single stream (test Acc 0.670 vs. 0.604 IR, 0.598 RGB) at real-time speed (at least 38 FPS). However, DBF is empirically indistinguishable from undiscounted averaging: near-zero inter-modal conflict on this presence-dominated benchmark leaves the discounting step inert. The fused uncertainty is well-calibrated (ECE 0.057) yet expectedly a weaker localization failure detector than spatial variance (AUROC 0.626 vs. 0.739). The null result is structural: the benchmark's near-universal presence and vacuous miss-encoding jointly suppress inter-modal conflict, a diagnosis that delimits where conflict-aware fusion provides measurable benefit.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-01 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.29235 — 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 2026-10-17T09:28:48.012Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).