{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5PLXCEI6TZ3TVQ3KI6UNAYKDQ6","short_pith_number":"pith:5PLXCEI6","schema_version":"1.0","canonical_sha256":"ebd771111e9e773ac36a47a8d0614387bc162d2ea2e21b394a857e9eb0aa31b3","source":{"kind":"arxiv","id":"2411.11370","version":2},"attestation_state":"computed","paper":{"title":"Transmission Line Defect Detection Based on UAV Patrol Images and Vision-language Pretraining","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiacun Wang, Jiyuan Yang, Ke Zhang, Yangjie Xiao, Yurong Guo, Zhaoye Zheng","submitted_at":"2024-11-18T08:32:51Z","abstract_excerpt":"Unmanned aerial vehicle (UAV) patrol inspection has emerged as a predominant approach in transmission line monitoring owing to its cost-effectiveness. Detecting defects in transmission lines is a critical task during UAV patrol inspection. However, due to imaging distance and shooting angles, UAV patrol images often suffer from insufficient defect-related visual information, which has an adverse effect on detection accuracy. In this article, we propose a novel method for detecting defects in UAV patrol images, which is based on vision-language pretraining for transmission line (VLP-TL) and a p"},"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":"2411.11370","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-18T08:32:51Z","cross_cats_sorted":[],"title_canon_sha256":"9e37ce8511f0b0bf94eee93710dcd578f6d10ae5aa3a9cf69a8da53cdce0c669","abstract_canon_sha256":"61f59cd0bc0bb688af79c380d88e868255f367421867288411c9fc2c4810764d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:01:28.834711Z","signature_b64":"NDjvMiMz0rQqijiYeb7xtztmO47An7phc85ofwao3+snj8GbS4pqagctYpFX7/71A1sbIgvms7ZcZcuUwARcBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ebd771111e9e773ac36a47a8d0614387bc162d2ea2e21b394a857e9eb0aa31b3","last_reissued_at":"2026-07-05T11:01:28.834134Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:01:28.834134Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transmission Line Defect Detection Based on UAV Patrol Images and Vision-language Pretraining","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiacun Wang, Jiyuan Yang, Ke Zhang, Yangjie Xiao, Yurong Guo, Zhaoye Zheng","submitted_at":"2024-11-18T08:32:51Z","abstract_excerpt":"Unmanned aerial vehicle (UAV) patrol inspection has emerged as a predominant approach in transmission line monitoring owing to its cost-effectiveness. Detecting defects in transmission lines is a critical task during UAV patrol inspection. However, due to imaging distance and shooting angles, UAV patrol images often suffer from insufficient defect-related visual information, which has an adverse effect on detection accuracy. In this article, we propose a novel method for detecting defects in UAV patrol images, which is based on vision-language pretraining for transmission line (VLP-TL) and a p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.11370","kind":"arxiv","version":2},"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/2411.11370/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":"2411.11370","created_at":"2026-07-05T11:01:28.834212+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.11370v2","created_at":"2026-07-05T11:01:28.834212+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.11370","created_at":"2026-07-05T11:01:28.834212+00:00"},{"alias_kind":"pith_short_12","alias_value":"5PLXCEI6TZ3T","created_at":"2026-07-05T11:01:28.834212+00:00"},{"alias_kind":"pith_short_16","alias_value":"5PLXCEI6TZ3TVQ3K","created_at":"2026-07-05T11:01:28.834212+00:00"},{"alias_kind":"pith_short_8","alias_value":"5PLXCEI6","created_at":"2026-07-05T11:01:28.834212+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/5PLXCEI6TZ3TVQ3KI6UNAYKDQ6","json":"https://pith.science/pith/5PLXCEI6TZ3TVQ3KI6UNAYKDQ6.json","graph_json":"https://pith.science/api/pith-number/5PLXCEI6TZ3TVQ3KI6UNAYKDQ6/graph.json","events_json":"https://pith.science/api/pith-number/5PLXCEI6TZ3TVQ3KI6UNAYKDQ6/events.json","paper":"https://pith.science/paper/5PLXCEI6"},"agent_actions":{"view_html":"https://pith.science/pith/5PLXCEI6TZ3TVQ3KI6UNAYKDQ6","download_json":"https://pith.science/pith/5PLXCEI6TZ3TVQ3KI6UNAYKDQ6.json","view_paper":"https://pith.science/paper/5PLXCEI6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.11370&json=true","fetch_graph":"https://pith.science/api/pith-number/5PLXCEI6TZ3TVQ3KI6UNAYKDQ6/graph.json","fetch_events":"https://pith.science/api/pith-number/5PLXCEI6TZ3TVQ3KI6UNAYKDQ6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5PLXCEI6TZ3TVQ3KI6UNAYKDQ6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5PLXCEI6TZ3TVQ3KI6UNAYKDQ6/action/storage_attestation","attest_author":"https://pith.science/pith/5PLXCEI6TZ3TVQ3KI6UNAYKDQ6/action/author_attestation","sign_citation":"https://pith.science/pith/5PLXCEI6TZ3TVQ3KI6UNAYKDQ6/action/citation_signature","submit_replication":"https://pith.science/pith/5PLXCEI6TZ3TVQ3KI6UNAYKDQ6/action/replication_record"}},"created_at":"2026-07-05T11:01:28.834212+00:00","updated_at":"2026-07-05T11:01:28.834212+00:00"}