{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:EKAA2X2BNWFY3LD4ZY43PGFK2V","short_pith_number":"pith:EKAA2X2B","schema_version":"1.0","canonical_sha256":"22800d5f416d8b8dac7cce39b798aad555f16e9383c06296f0ad792eece0550f","source":{"kind":"arxiv","id":"2605.15326","version":1},"attestation_state":"computed","paper":{"title":"Multimodal Object Detection Under Sparse Forest-Canopy Occlusion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Multimodal fusion of thermal-visible imagery and airborne optical sectioning improves human detection under sparse forest canopy where LiDAR penetration proves limited.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mangal Kothari, Nitik Jain","submitted_at":"2026-05-14T18:39:51Z","abstract_excerpt":"Reliable detection of humans beneath forest canopy remains a difficult remote-sensing challenge due to sparse, structured, and viewpoint-dependent occlusion. This paper presents a multimodal proof-of-concept pipeline that integrates three complementary approaches: (i) experimental evaluation of LiDAR returns through vegetation to assess the feasibility of active sensing, (ii) visible--thermal image fusion using a multi-scale transform and sparse-representation framework to enhance human saliency, and (iii) synthetic-aperture image formation via Airborne Optical Sectioning (AOS) to suppress can"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":true,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.15326","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-14T18:39:51Z","cross_cats_sorted":[],"title_canon_sha256":"0ae236e8ec9937cc74a045c579033a4fd18282f9256aab6bf9cde185acff0d22","abstract_canon_sha256":"0900e4d39d3033390a065daefe8d4d990928d0dc4022edecefeb9bfb7b4cde50"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:00:52.764060Z","signature_b64":"mQCBSIfvuuWHawT053T0Z+uiNSS+aYPgG3ynm4e7K7/zfVAkFHzSSmCwFY4bg7dJLBnoKHM8VQTBI5xzmhznCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22800d5f416d8b8dac7cce39b798aad555f16e9383c06296f0ad792eece0550f","last_reissued_at":"2026-05-20T00:00:52.763357Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:00:52.763357Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multimodal Object Detection Under Sparse Forest-Canopy Occlusion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Multimodal fusion of thermal-visible imagery and airborne optical sectioning improves human detection under sparse forest canopy where LiDAR penetration proves limited.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mangal Kothari, Nitik Jain","submitted_at":"2026-05-14T18:39:51Z","abstract_excerpt":"Reliable detection of humans beneath forest canopy remains a difficult remote-sensing challenge due to sparse, structured, and viewpoint-dependent occlusion. This paper presents a multimodal proof-of-concept pipeline that integrates three complementary approaches: (i) experimental evaluation of LiDAR returns through vegetation to assess the feasibility of active sensing, (ii) visible--thermal image fusion using a multi-scale transform and sparse-representation framework to enhance human saliency, and (iii) synthetic-aperture image formation via Airborne Optical Sectioning (AOS) to suppress can"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"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).","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"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.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Multimodal fusion of thermal-visible imagery and airborne optical sectioning improves human detection under sparse forest canopy where LiDAR penetration proves limited.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"740ad302ee08c7c3e425c26742a324eeea8fcaffbe273dff79ac922d2fb6a36e"},"source":{"id":"2605.15326","kind":"arxiv","version":1},"verdict":{"id":"4a9905e6-0b46-4dda-ab61-f29d66db2f7b","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T16:03:47.644895Z","strongest_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.","one_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.","pipeline_version":"pith-pipeline@v0.9.0","weakest_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).","pith_extraction_headline":"Multimodal fusion of thermal-visible imagery and airborne optical sectioning improves human detection under sparse forest canopy where LiDAR penetration proves limited."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15326/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T16:31:18.294414Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T16:16:01.154453Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T14:41:54.199758Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.765016Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"7b88d969798fb16252c6710591991c5ab1c7290d6d0f7550562588f0f675e9a7"},"references":{"count":10,"sample":[{"doi":"","year":2018,"title":"Survey of computer vision algorithms and applications for unmanned aerial vehicles,","work_id":"95f61f48-b142-4b88-8a60-1946153d1f38","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2016,"title":"You Only Look Once: Unified, Real-Time Object Detection,","work_id":"0c9c853a-a296-4362-aa28-d84242ec77b2","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2018,"title":"YOLOv3: An Incremental Improvement","work_id":"d737b3cc-9bd1-43d6-8310-c91e64b510f7","ref_index":3,"cited_arxiv_id":"1804.02767","is_internal_anchor":true},{"doi":"","year":2020,"title":"VIFB: A Visible and Infrared Image Fusion Benchmark,","work_id":"38bbcc5d-13ca-4b48-9828-016de6f33ee4","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2016,"title":"Image Fusion with Convolutional Sparse Representation,","work_id":"db9f1ad4-63e7-4a08-b2c0-8460e7d4dc77","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":10,"snapshot_sha256":"26a75a9785a2ff6ec38b80da59d31e3cc36dd7e76c7fc08048e4103d292ac50c","internal_anchors":1},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.15326","created_at":"2026-05-20T00:00:52.763478+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.15326v1","created_at":"2026-05-20T00:00:52.763478+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15326","created_at":"2026-05-20T00:00:52.763478+00:00"},{"alias_kind":"pith_short_12","alias_value":"EKAA2X2BNWFY","created_at":"2026-05-20T00:00:52.763478+00:00"},{"alias_kind":"pith_short_16","alias_value":"EKAA2X2BNWFY3LD4","created_at":"2026-05-20T00:00:52.763478+00:00"},{"alias_kind":"pith_short_8","alias_value":"EKAA2X2B","created_at":"2026-05-20T00:00:52.763478+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EKAA2X2BNWFY3LD4ZY43PGFK2V","json":"https://pith.science/pith/EKAA2X2BNWFY3LD4ZY43PGFK2V.json","graph_json":"https://pith.science/api/pith-number/EKAA2X2BNWFY3LD4ZY43PGFK2V/graph.json","events_json":"https://pith.science/api/pith-number/EKAA2X2BNWFY3LD4ZY43PGFK2V/events.json","paper":"https://pith.science/paper/EKAA2X2B"},"agent_actions":{"view_html":"https://pith.science/pith/EKAA2X2BNWFY3LD4ZY43PGFK2V","download_json":"https://pith.science/pith/EKAA2X2BNWFY3LD4ZY43PGFK2V.json","view_paper":"https://pith.science/paper/EKAA2X2B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.15326&json=true","fetch_graph":"https://pith.science/api/pith-number/EKAA2X2BNWFY3LD4ZY43PGFK2V/graph.json","fetch_events":"https://pith.science/api/pith-number/EKAA2X2BNWFY3LD4ZY43PGFK2V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EKAA2X2BNWFY3LD4ZY43PGFK2V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EKAA2X2BNWFY3LD4ZY43PGFK2V/action/storage_attestation","attest_author":"https://pith.science/pith/EKAA2X2BNWFY3LD4ZY43PGFK2V/action/author_attestation","sign_citation":"https://pith.science/pith/EKAA2X2BNWFY3LD4ZY43PGFK2V/action/citation_signature","submit_replication":"https://pith.science/pith/EKAA2X2BNWFY3LD4ZY43PGFK2V/action/replication_record"}},"created_at":"2026-05-20T00:00:52.763478+00:00","updated_at":"2026-05-20T00:00:52.763478+00:00"}