{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HT2TEZGPJ44UDJZS27ITFNULVC","short_pith_number":"pith:HT2TEZGP","schema_version":"1.0","canonical_sha256":"3cf53264cf4f3941a732d7d132b68ba89aa7c2469d10e2e231f56448a863a380","source":{"kind":"arxiv","id":"2410.20953","version":1},"attestation_state":"computed","paper":{"title":"IndraEye: Infrared Electro-Optical UAV-based Perception Dataset for Robust Downstream Tasks","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aditya Gandhamal, Aniruddh Sikdar, Manjunath D, Prajwal Gurunath, Shrikar Madhu, Sumanth Udupa, Suresh Sundaram","submitted_at":"2024-10-28T12:12:28Z","abstract_excerpt":"Deep neural networks (DNNs) have shown exceptional performance when trained on well-illuminated images captured by Electro-Optical (EO) cameras, which provide rich texture details. However, in critical applications like aerial perception, it is essential for DNNs to maintain consistent reliability across all conditions, including low-light scenarios where EO cameras often struggle to capture sufficient detail. Additionally, UAV-based aerial object detection faces significant challenges due to scale variability from varying altitudes and slant angles, adding another layer of complexity. Existin"},"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":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2410.20953","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-28T12:12:28Z","cross_cats_sorted":[],"title_canon_sha256":"6a1fdba41d2c865b6b55b436d45a57ed82f35d6ed04a6ed4ff397afcfada4d48","abstract_canon_sha256":"98385bed4083d38b6aebdb4370d36275453b8e9a4fa4bd2ce3ed77cc5dda8f6c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:27:13.473197Z","signature_b64":"FESEzRMJxJQXy8hyU8mmeBrKejOe/2wrdXTe9x4lG02wyGuwDA4NZG8YJbWUWHKej1781k2s+DDLaLkVU+OGAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3cf53264cf4f3941a732d7d132b68ba89aa7c2469d10e2e231f56448a863a380","last_reissued_at":"2026-07-05T09:27:13.472795Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:27:13.472795Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"IndraEye: Infrared Electro-Optical UAV-based Perception Dataset for Robust Downstream Tasks","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aditya Gandhamal, Aniruddh Sikdar, Manjunath D, Prajwal Gurunath, Shrikar Madhu, Sumanth Udupa, Suresh Sundaram","submitted_at":"2024-10-28T12:12:28Z","abstract_excerpt":"Deep neural networks (DNNs) have shown exceptional performance when trained on well-illuminated images captured by Electro-Optical (EO) cameras, which provide rich texture details. However, in critical applications like aerial perception, it is essential for DNNs to maintain consistent reliability across all conditions, including low-light scenarios where EO cameras often struggle to capture sufficient detail. Additionally, UAV-based aerial object detection faces significant challenges due to scale variability from varying altitudes and slant angles, adding another layer of complexity. Existin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.20953","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2410.20953/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"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":"2410.20953","created_at":"2026-07-05T09:27:13.472851+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.20953v1","created_at":"2026-07-05T09:27:13.472851+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.20953","created_at":"2026-07-05T09:27:13.472851+00:00"},{"alias_kind":"pith_short_12","alias_value":"HT2TEZGPJ44U","created_at":"2026-07-05T09:27:13.472851+00:00"},{"alias_kind":"pith_short_16","alias_value":"HT2TEZGPJ44UDJZS","created_at":"2026-07-05T09:27:13.472851+00:00"},{"alias_kind":"pith_short_8","alias_value":"HT2TEZGP","created_at":"2026-07-05T09:27:13.472851+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/HT2TEZGPJ44UDJZS27ITFNULVC","json":"https://pith.science/pith/HT2TEZGPJ44UDJZS27ITFNULVC.json","graph_json":"https://pith.science/api/pith-number/HT2TEZGPJ44UDJZS27ITFNULVC/graph.json","events_json":"https://pith.science/api/pith-number/HT2TEZGPJ44UDJZS27ITFNULVC/events.json","paper":"https://pith.science/paper/HT2TEZGP"},"agent_actions":{"view_html":"https://pith.science/pith/HT2TEZGPJ44UDJZS27ITFNULVC","download_json":"https://pith.science/pith/HT2TEZGPJ44UDJZS27ITFNULVC.json","view_paper":"https://pith.science/paper/HT2TEZGP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.20953&json=true","fetch_graph":"https://pith.science/api/pith-number/HT2TEZGPJ44UDJZS27ITFNULVC/graph.json","fetch_events":"https://pith.science/api/pith-number/HT2TEZGPJ44UDJZS27ITFNULVC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HT2TEZGPJ44UDJZS27ITFNULVC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HT2TEZGPJ44UDJZS27ITFNULVC/action/storage_attestation","attest_author":"https://pith.science/pith/HT2TEZGPJ44UDJZS27ITFNULVC/action/author_attestation","sign_citation":"https://pith.science/pith/HT2TEZGPJ44UDJZS27ITFNULVC/action/citation_signature","submit_replication":"https://pith.science/pith/HT2TEZGPJ44UDJZS27ITFNULVC/action/replication_record"}},"created_at":"2026-07-05T09:27:13.472851+00:00","updated_at":"2026-07-05T09:27:13.472851+00:00"}