{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZKVBVY7TGT674UAD4734SXY535","short_pith_number":"pith:ZKVBVY7T","schema_version":"1.0","canonical_sha256":"caaa1ae3f334fdfe5003e7f7c95f1ddf4419d932d44cc5e5e93b0ed28e6abb88","source":{"kind":"arxiv","id":"2505.16740","version":1},"attestation_state":"computed","paper":{"title":"Robust Vision-Based Runway Detection through Conformal Prediction and Conformal mAP","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Alya Zouzou, L\\'eo And\\'eol, M\\'elanie Ducoffe, Ryma Boumazouza","submitted_at":"2025-05-22T14:52:59Z","abstract_excerpt":"We explore the use of conformal prediction to provide statistical uncertainty guarantees for runway detection in vision-based landing systems (VLS). Using fine-tuned YOLOv5 and YOLOv6 models on aerial imagery, we apply conformal prediction to quantify localization reliability under user-defined risk levels. We also introduce Conformal mean Average Precision (C-mAP), a novel metric aligning object detection performance with conformal guarantees. Our results show that conformal prediction can improve the reliability of runway detection by quantifying uncertainty in a statistically sound way, inc"},"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":"2505.16740","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-22T14:52:59Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1efadeaa62b6e27037f03b64ef7671e6f29da6d6db5887e5e6e6bee6eb0240ae","abstract_canon_sha256":"5efdbd088e251349181aedd76c4bd8fccb11a3176be15b731050cb12ed53c378"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:07:39.993345Z","signature_b64":"PSevzg7AsB9gPeIzJxSncyODH5IDkgULbzLoo3sOGdp6NoQm2mKgwozK3KxxetqX9hxyWFWZTsGEjqpAjx45DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"caaa1ae3f334fdfe5003e7f7c95f1ddf4419d932d44cc5e5e93b0ed28e6abb88","last_reissued_at":"2026-07-05T11:07:39.992809Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:07:39.992809Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust Vision-Based Runway Detection through Conformal Prediction and Conformal mAP","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Alya Zouzou, L\\'eo And\\'eol, M\\'elanie Ducoffe, Ryma Boumazouza","submitted_at":"2025-05-22T14:52:59Z","abstract_excerpt":"We explore the use of conformal prediction to provide statistical uncertainty guarantees for runway detection in vision-based landing systems (VLS). Using fine-tuned YOLOv5 and YOLOv6 models on aerial imagery, we apply conformal prediction to quantify localization reliability under user-defined risk levels. We also introduce Conformal mean Average Precision (C-mAP), a novel metric aligning object detection performance with conformal guarantees. Our results show that conformal prediction can improve the reliability of runway detection by quantifying uncertainty in a statistically sound way, inc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.16740","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/2505.16740/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":"2505.16740","created_at":"2026-07-05T11:07:39.992887+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.16740v1","created_at":"2026-07-05T11:07:39.992887+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.16740","created_at":"2026-07-05T11:07:39.992887+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZKVBVY7TGT67","created_at":"2026-07-05T11:07:39.992887+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZKVBVY7TGT674UAD","created_at":"2026-07-05T11:07:39.992887+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZKVBVY7T","created_at":"2026-07-05T11:07:39.992887+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.26411","citing_title":"Unifying Runtime Monitoring Approaches for Safety-Critical Machine Learning: Application to Vision-Based Landing","ref_index":49,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZKVBVY7TGT674UAD4734SXY535","json":"https://pith.science/pith/ZKVBVY7TGT674UAD4734SXY535.json","graph_json":"https://pith.science/api/pith-number/ZKVBVY7TGT674UAD4734SXY535/graph.json","events_json":"https://pith.science/api/pith-number/ZKVBVY7TGT674UAD4734SXY535/events.json","paper":"https://pith.science/paper/ZKVBVY7T"},"agent_actions":{"view_html":"https://pith.science/pith/ZKVBVY7TGT674UAD4734SXY535","download_json":"https://pith.science/pith/ZKVBVY7TGT674UAD4734SXY535.json","view_paper":"https://pith.science/paper/ZKVBVY7T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.16740&json=true","fetch_graph":"https://pith.science/api/pith-number/ZKVBVY7TGT674UAD4734SXY535/graph.json","fetch_events":"https://pith.science/api/pith-number/ZKVBVY7TGT674UAD4734SXY535/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZKVBVY7TGT674UAD4734SXY535/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZKVBVY7TGT674UAD4734SXY535/action/storage_attestation","attest_author":"https://pith.science/pith/ZKVBVY7TGT674UAD4734SXY535/action/author_attestation","sign_citation":"https://pith.science/pith/ZKVBVY7TGT674UAD4734SXY535/action/citation_signature","submit_replication":"https://pith.science/pith/ZKVBVY7TGT674UAD4734SXY535/action/replication_record"}},"created_at":"2026-07-05T11:07:39.992887+00:00","updated_at":"2026-07-05T11:07:39.992887+00:00"}