Pith Number

pith:EKAA2X2B

pith:2026:EKAA2X2BNWFY3LD4ZY43PGFK2V

not attested not anchored not stored refs resolved

Multimodal Object Detection Under Sparse Forest-Canopy Occlusion

Mangal Kothari, Nitik Jain

Multimodal fusion of thermal-visible imagery and airborne optical sectioning improves human detection under sparse forest canopy where LiDAR penetration proves limited.

arxiv:2605.15326 v1 · 2026-05-14 · cs.CV

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{EKAA2X2BNWFY3LD4ZY43PGFK2V}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Results show that the tested terrestrial LiDAR configuration provides limited penetration for object-level detection, while visible-thermal fusion improves target visibility in low-contrast scenes and AOS enhances ground-plane detection in synthetic forest imagery. The fine-tuned YOLOv5 achieves a mean average precision of ~0.83 on the top three FLIR classes.

C2weakest assumption

The assumption that performance on the Teledyne FLIR thermal dataset and synthetic forest imagery will translate to real-world sparse forest-canopy occlusion scenarios with actual UAV or ground-based captures (abstract, results paragraph).

C3one line summary

A proof-of-concept multimodal pipeline using LiDAR, visible-thermal fusion, AOS, and fine-tuned YOLOv5 reports mAP of ~0.83 on thermal classes and notes limited LiDAR penetration plus improved visibility from fusion and AOS in forest settings.

References

10 extracted · 10 resolved · 1 Pith anchors

[1] Survey of computer vision algorithms and applications for unmanned aerial vehicles, 2018

[2] You Only Look Once: Unified, Real-Time Object Detection, 2016

[3] YOLOv3: An Incremental Improvement 2018 · arXiv:1804.02767

[4] VIFB: A Visible and Infrared Image Fusion Benchmark, 2020

[5] Image Fusion with Convolutional Sparse Representation, 2016

Receipt and verification

First computed	2026-05-20T00:00:52.763357Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

22800d5f416d8b8dac7cce39b798aad555f16e9383c06296f0ad792eece0550f

Aliases

arxiv: 2605.15326 · arxiv_version: 2605.15326v1 · doi: 10.48550/arxiv.2605.15326 · pith_short_12: EKAA2X2BNWFY · pith_short_16: EKAA2X2BNWFY3LD4 · pith_short_8: EKAA2X2B

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/EKAA2X2BNWFY3LD4ZY43PGFK2V \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 22800d5f416d8b8dac7cce39b798aad555f16e9383c06296f0ad792eece0550f

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "0900e4d39d3033390a065daefe8d4d990928d0dc4022edecefeb9bfb7b4cde50",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T18:39:51Z",
    "title_canon_sha256": "0ae236e8ec9937cc74a045c579033a4fd18282f9256aab6bf9cde185acff0d22"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15326",
    "kind": "arxiv",
    "version": 1
  }
}