{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:XY7WGBCCLY4BWSJAYPTJPPA3OV","short_pith_number":"pith:XY7WGBCC","schema_version":"1.0","canonical_sha256":"be3f6304425e381b4920c3e697bc1b754afcb273b1688ef3a2a93cb6852b9b34","source":{"kind":"arxiv","id":"2309.06750","version":1},"attestation_state":"computed","paper":{"title":"MFL-YOLO: An Object Detection Model for Damaged Traffic Signs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiangtao Ren, Tengyang Chen","submitted_at":"2023-09-13T06:46:27Z","abstract_excerpt":"Traffic signs are important facilities to ensure traffic safety and smooth flow, but may be damaged due to many reasons, which poses a great safety hazard. Therefore, it is important to study a method to detect damaged traffic signs. Existing object detection techniques for damaged traffic signs are still absent. Since damaged traffic signs are closer in appearance to normal ones, it is difficult to capture the detailed local damage features of damaged traffic signs using traditional object detection methods. In this paper, we propose an improved object detection method based on YOLOv5s, namel"},"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":"2309.06750","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-09-13T06:46:27Z","cross_cats_sorted":[],"title_canon_sha256":"6f36dbad5eba9f005f5ab1553feaff5aec42efc8b1c74ba8767a089b7aa30416","abstract_canon_sha256":"f32ab1f912c7bb801d93bbf93194492f4c4132852f00b6e656ad5a447f2167e5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:50:22.025835Z","signature_b64":"+dwL0a3et3+ZEgnU+RQ7qhb3/nMEK38oa1SExRUfExSGG4SxOsmGShlHFB5kb3PDitVMFmVepXBw3Phv3m/yAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be3f6304425e381b4920c3e697bc1b754afcb273b1688ef3a2a93cb6852b9b34","last_reissued_at":"2026-07-05T06:50:22.025381Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:50:22.025381Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MFL-YOLO: An Object Detection Model for Damaged Traffic Signs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiangtao Ren, Tengyang Chen","submitted_at":"2023-09-13T06:46:27Z","abstract_excerpt":"Traffic signs are important facilities to ensure traffic safety and smooth flow, but may be damaged due to many reasons, which poses a great safety hazard. Therefore, it is important to study a method to detect damaged traffic signs. Existing object detection techniques for damaged traffic signs are still absent. Since damaged traffic signs are closer in appearance to normal ones, it is difficult to capture the detailed local damage features of damaged traffic signs using traditional object detection methods. In this paper, we propose an improved object detection method based on YOLOv5s, namel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.06750","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/2309.06750/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":"2309.06750","created_at":"2026-07-05T06:50:22.025439+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.06750v1","created_at":"2026-07-05T06:50:22.025439+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.06750","created_at":"2026-07-05T06:50:22.025439+00:00"},{"alias_kind":"pith_short_12","alias_value":"XY7WGBCCLY4B","created_at":"2026-07-05T06:50:22.025439+00:00"},{"alias_kind":"pith_short_16","alias_value":"XY7WGBCCLY4BWSJA","created_at":"2026-07-05T06:50:22.025439+00:00"},{"alias_kind":"pith_short_8","alias_value":"XY7WGBCC","created_at":"2026-07-05T06:50:22.025439+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.04289","citing_title":"YOLO-CCA: A Context-Based Approach for Traffic Sign Detection","ref_index":34,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XY7WGBCCLY4BWSJAYPTJPPA3OV","json":"https://pith.science/pith/XY7WGBCCLY4BWSJAYPTJPPA3OV.json","graph_json":"https://pith.science/api/pith-number/XY7WGBCCLY4BWSJAYPTJPPA3OV/graph.json","events_json":"https://pith.science/api/pith-number/XY7WGBCCLY4BWSJAYPTJPPA3OV/events.json","paper":"https://pith.science/paper/XY7WGBCC"},"agent_actions":{"view_html":"https://pith.science/pith/XY7WGBCCLY4BWSJAYPTJPPA3OV","download_json":"https://pith.science/pith/XY7WGBCCLY4BWSJAYPTJPPA3OV.json","view_paper":"https://pith.science/paper/XY7WGBCC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.06750&json=true","fetch_graph":"https://pith.science/api/pith-number/XY7WGBCCLY4BWSJAYPTJPPA3OV/graph.json","fetch_events":"https://pith.science/api/pith-number/XY7WGBCCLY4BWSJAYPTJPPA3OV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XY7WGBCCLY4BWSJAYPTJPPA3OV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XY7WGBCCLY4BWSJAYPTJPPA3OV/action/storage_attestation","attest_author":"https://pith.science/pith/XY7WGBCCLY4BWSJAYPTJPPA3OV/action/author_attestation","sign_citation":"https://pith.science/pith/XY7WGBCCLY4BWSJAYPTJPPA3OV/action/citation_signature","submit_replication":"https://pith.science/pith/XY7WGBCCLY4BWSJAYPTJPPA3OV/action/replication_record"}},"created_at":"2026-07-05T06:50:22.025439+00:00","updated_at":"2026-07-05T06:50:22.025439+00:00"}