{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:Y4IEM7ST7WWQ2XG3QID7F5OP45","short_pith_number":"pith:Y4IEM7ST","schema_version":"1.0","canonical_sha256":"c710467e53fdad0d5cdb8207f2f5cfe759e2f3cfc37d629cc6c2842ca998a97e","source":{"kind":"arxiv","id":"2504.20032","version":1},"attestation_state":"computed","paper":{"title":"More Clear, More Flexible, More Precise: A Comprehensive Oriented Object Detection benchmark for UAV","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bowen Liu, Haidi Tang, Kai Ye, Liujuan Cao, Pingyang Dai, Rongrong Ji","submitted_at":"2025-04-28T17:56:02Z","abstract_excerpt":"Applications of unmanned aerial vehicle (UAV) in logistics, agricultural automation, urban management, and emergency response are highly dependent on oriented object detection (OOD) to enhance visual perception. Although existing datasets for OOD in UAV provide valuable resources, they are often designed for specific downstream tasks.Consequently, they exhibit limited generalization performance in real flight scenarios and fail to thoroughly demonstrate algorithm effectiveness in practical environments. To bridge this critical gap, we introduce CODrone, a comprehensive oriented object detectio"},"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":"2504.20032","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-04-28T17:56:02Z","cross_cats_sorted":[],"title_canon_sha256":"358b83dd6dc3f2dcc1f80088e84a346f552965927aa3caa229497f1658910c3d","abstract_canon_sha256":"a2e181c97678cae62e13bf47104c520c572222f528000ed29287d09abdde5b50"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:55:08.089082Z","signature_b64":"ZIrXHqUFx/H0QcGTVCC9ECPwjXTCtOgr5YAj4N8MIteLaRrYcggClMvyFX17dbPFVFLvbNh7NbQDNCzlRNLJAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c710467e53fdad0d5cdb8207f2f5cfe759e2f3cfc37d629cc6c2842ca998a97e","last_reissued_at":"2026-07-05T10:55:08.088637Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:55:08.088637Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"More Clear, More Flexible, More Precise: A Comprehensive Oriented Object Detection benchmark for UAV","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bowen Liu, Haidi Tang, Kai Ye, Liujuan Cao, Pingyang Dai, Rongrong Ji","submitted_at":"2025-04-28T17:56:02Z","abstract_excerpt":"Applications of unmanned aerial vehicle (UAV) in logistics, agricultural automation, urban management, and emergency response are highly dependent on oriented object detection (OOD) to enhance visual perception. Although existing datasets for OOD in UAV provide valuable resources, they are often designed for specific downstream tasks.Consequently, they exhibit limited generalization performance in real flight scenarios and fail to thoroughly demonstrate algorithm effectiveness in practical environments. To bridge this critical gap, we introduce CODrone, a comprehensive oriented object detectio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.20032","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/2504.20032/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":"2504.20032","created_at":"2026-07-05T10:55:08.088690+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.20032v1","created_at":"2026-07-05T10:55:08.088690+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.20032","created_at":"2026-07-05T10:55:08.088690+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y4IEM7ST7WWQ","created_at":"2026-07-05T10:55:08.088690+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y4IEM7ST7WWQ2XG3","created_at":"2026-07-05T10:55:08.088690+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y4IEM7ST","created_at":"2026-07-05T10:55:08.088690+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.15670","citing_title":"PixDLM: A Dual-Path Multimodal Language Model for UAV Reasoning Segmentation","ref_index":56,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Y4IEM7ST7WWQ2XG3QID7F5OP45","json":"https://pith.science/pith/Y4IEM7ST7WWQ2XG3QID7F5OP45.json","graph_json":"https://pith.science/api/pith-number/Y4IEM7ST7WWQ2XG3QID7F5OP45/graph.json","events_json":"https://pith.science/api/pith-number/Y4IEM7ST7WWQ2XG3QID7F5OP45/events.json","paper":"https://pith.science/paper/Y4IEM7ST"},"agent_actions":{"view_html":"https://pith.science/pith/Y4IEM7ST7WWQ2XG3QID7F5OP45","download_json":"https://pith.science/pith/Y4IEM7ST7WWQ2XG3QID7F5OP45.json","view_paper":"https://pith.science/paper/Y4IEM7ST","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.20032&json=true","fetch_graph":"https://pith.science/api/pith-number/Y4IEM7ST7WWQ2XG3QID7F5OP45/graph.json","fetch_events":"https://pith.science/api/pith-number/Y4IEM7ST7WWQ2XG3QID7F5OP45/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y4IEM7ST7WWQ2XG3QID7F5OP45/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y4IEM7ST7WWQ2XG3QID7F5OP45/action/storage_attestation","attest_author":"https://pith.science/pith/Y4IEM7ST7WWQ2XG3QID7F5OP45/action/author_attestation","sign_citation":"https://pith.science/pith/Y4IEM7ST7WWQ2XG3QID7F5OP45/action/citation_signature","submit_replication":"https://pith.science/pith/Y4IEM7ST7WWQ2XG3QID7F5OP45/action/replication_record"}},"created_at":"2026-07-05T10:55:08.088690+00:00","updated_at":"2026-07-05T10:55:08.088690+00:00"}