{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:QH5DS2TCLOWAFN5WWJ2PE34EPR","short_pith_number":"pith:QH5DS2TC","schema_version":"1.0","canonical_sha256":"81fa396a625bac02b7b6b274f26f847c58ad8b77748116b30ef672987ac689ca","source":{"kind":"arxiv","id":"1811.09473","version":1},"attestation_state":"computed","paper":{"title":"Defect Detection from UAV Images based on Region-Based CNNs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bo Du, Lefei Zhang, Meng Lan, Yipeng Zhang","submitted_at":"2018-11-23T13:38:17Z","abstract_excerpt":"With the wide applications of Unmanned Aerial Vehicle (UAV) in engineering such as the inspection of the electrical equipment from distance, the demands of efficient object detection algorithms for abundant images acquired by UAV have also been significantly increased in recent years. In this work, we study the performance of the region-based CNN for the electrical equipment defect detection by using the UAV images. In order to train the detection model, we collect a UAV images dataset composes of four classes of electrical equipment defects with thousands of annotated labels. Then, based on t"},"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":"1811.09473","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-23T13:38:17Z","cross_cats_sorted":[],"title_canon_sha256":"ff57b1fff4f1fa6c89509127391776329761ed4c497fa53246df843eac0c039d","abstract_canon_sha256":"b32f8419e66c01394b33810aaf648e594253ed55fb232b250160ed3fb78b4479"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:03.335351Z","signature_b64":"axcBQpQnFtTcSqo0epFSsTrJPL+Ab2HrneL/VcP/BFH5sM+ZZaSlvqboNm6Eywu5qlUCiWr/aUqusLlWDBOSBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81fa396a625bac02b7b6b274f26f847c58ad8b77748116b30ef672987ac689ca","last_reissued_at":"2026-05-18T00:00:03.334809Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:03.334809Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Defect Detection from UAV Images based on Region-Based CNNs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bo Du, Lefei Zhang, Meng Lan, Yipeng Zhang","submitted_at":"2018-11-23T13:38:17Z","abstract_excerpt":"With the wide applications of Unmanned Aerial Vehicle (UAV) in engineering such as the inspection of the electrical equipment from distance, the demands of efficient object detection algorithms for abundant images acquired by UAV have also been significantly increased in recent years. In this work, we study the performance of the region-based CNN for the electrical equipment defect detection by using the UAV images. In order to train the detection model, we collect a UAV images dataset composes of four classes of electrical equipment defects with thousands of annotated labels. Then, based on t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.09473","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":""},"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":"1811.09473","created_at":"2026-05-18T00:00:03.334900+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.09473v1","created_at":"2026-05-18T00:00:03.334900+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.09473","created_at":"2026-05-18T00:00:03.334900+00:00"},{"alias_kind":"pith_short_12","alias_value":"QH5DS2TCLOWA","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"QH5DS2TCLOWAFN5W","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"QH5DS2TC","created_at":"2026-05-18T12:32:46.962924+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/QH5DS2TCLOWAFN5WWJ2PE34EPR","json":"https://pith.science/pith/QH5DS2TCLOWAFN5WWJ2PE34EPR.json","graph_json":"https://pith.science/api/pith-number/QH5DS2TCLOWAFN5WWJ2PE34EPR/graph.json","events_json":"https://pith.science/api/pith-number/QH5DS2TCLOWAFN5WWJ2PE34EPR/events.json","paper":"https://pith.science/paper/QH5DS2TC"},"agent_actions":{"view_html":"https://pith.science/pith/QH5DS2TCLOWAFN5WWJ2PE34EPR","download_json":"https://pith.science/pith/QH5DS2TCLOWAFN5WWJ2PE34EPR.json","view_paper":"https://pith.science/paper/QH5DS2TC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.09473&json=true","fetch_graph":"https://pith.science/api/pith-number/QH5DS2TCLOWAFN5WWJ2PE34EPR/graph.json","fetch_events":"https://pith.science/api/pith-number/QH5DS2TCLOWAFN5WWJ2PE34EPR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QH5DS2TCLOWAFN5WWJ2PE34EPR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QH5DS2TCLOWAFN5WWJ2PE34EPR/action/storage_attestation","attest_author":"https://pith.science/pith/QH5DS2TCLOWAFN5WWJ2PE34EPR/action/author_attestation","sign_citation":"https://pith.science/pith/QH5DS2TCLOWAFN5WWJ2PE34EPR/action/citation_signature","submit_replication":"https://pith.science/pith/QH5DS2TCLOWAFN5WWJ2PE34EPR/action/replication_record"}},"created_at":"2026-05-18T00:00:03.334900+00:00","updated_at":"2026-05-18T00:00:03.334900+00:00"}