{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZKHC677N4SW5HP2JU7VFVP4QD5","short_pith_number":"pith:ZKHC677N","schema_version":"1.0","canonical_sha256":"ca8e2f7fede4add3bf49a7ea5abf901f65d5016df0805c555ea4de7d5b941d35","source":{"kind":"arxiv","id":"2503.18082","version":1},"attestation_state":"computed","paper":{"title":"Vehicular Road Crack Detection with Deep Learning: A New Online Benchmark for Comprehensive Evaluation of Existing Algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Chuang-Wei Liu, Lihua Xie, Nachuan Ma, Qiang Hu, Rui Fan, Yanting Zhang, Yu Han, Zhengfei Song","submitted_at":"2025-03-23T14:26:18Z","abstract_excerpt":"In the emerging field of urban digital twins (UDTs), advancing intelligent road inspection (IRI) vehicles with automatic road crack detection systems is essential for maintaining civil infrastructure. Over the past decade, deep learning-based road crack detection methods have been developed to detect cracks more efficiently, accurately, and objectively, with the goal of replacing manual visual inspection. Nonetheless, there is a lack of systematic reviews on state-of-the-art (SoTA) deep learning techniques, especially data-fusion and label-efficient algorithms for this task. This paper thoroug"},"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":"2503.18082","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-23T14:26:18Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"4e849631fb4f4682d46bae8207a33bb2a0f073423cbb68c9eea36b49e5f194fc","abstract_canon_sha256":"e0ef7e18b13f27d9ca270b200ce1c6ffded4bfe5bf6d51d43822935d7798012c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:38:02.287249Z","signature_b64":"81oyQuQ6chL+7dq1jlA5C+RdCOPcxXCqjPJ9PvX4YvhVVsxQXABYPSDmbYXGk1q+PG4xTXNgEqVbXS9rG5caCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ca8e2f7fede4add3bf49a7ea5abf901f65d5016df0805c555ea4de7d5b941d35","last_reissued_at":"2026-07-05T10:38:02.286419Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:38:02.286419Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Vehicular Road Crack Detection with Deep Learning: A New Online Benchmark for Comprehensive Evaluation of Existing Algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Chuang-Wei Liu, Lihua Xie, Nachuan Ma, Qiang Hu, Rui Fan, Yanting Zhang, Yu Han, Zhengfei Song","submitted_at":"2025-03-23T14:26:18Z","abstract_excerpt":"In the emerging field of urban digital twins (UDTs), advancing intelligent road inspection (IRI) vehicles with automatic road crack detection systems is essential for maintaining civil infrastructure. Over the past decade, deep learning-based road crack detection methods have been developed to detect cracks more efficiently, accurately, and objectively, with the goal of replacing manual visual inspection. Nonetheless, there is a lack of systematic reviews on state-of-the-art (SoTA) deep learning techniques, especially data-fusion and label-efficient algorithms for this task. This paper thoroug"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.18082","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/2503.18082/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":"2503.18082","created_at":"2026-07-05T10:38:02.286525+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.18082v1","created_at":"2026-07-05T10:38:02.286525+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.18082","created_at":"2026-07-05T10:38:02.286525+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZKHC677N4SW5","created_at":"2026-07-05T10:38:02.286525+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZKHC677N4SW5HP2J","created_at":"2026-07-05T10:38:02.286525+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZKHC677N","created_at":"2026-07-05T10:38:02.286525+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.24616","citing_title":"Infrastructure-Guided Connectivity-Enhanced Road Crack Detection and Estimation","ref_index":34,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZKHC677N4SW5HP2JU7VFVP4QD5","json":"https://pith.science/pith/ZKHC677N4SW5HP2JU7VFVP4QD5.json","graph_json":"https://pith.science/api/pith-number/ZKHC677N4SW5HP2JU7VFVP4QD5/graph.json","events_json":"https://pith.science/api/pith-number/ZKHC677N4SW5HP2JU7VFVP4QD5/events.json","paper":"https://pith.science/paper/ZKHC677N"},"agent_actions":{"view_html":"https://pith.science/pith/ZKHC677N4SW5HP2JU7VFVP4QD5","download_json":"https://pith.science/pith/ZKHC677N4SW5HP2JU7VFVP4QD5.json","view_paper":"https://pith.science/paper/ZKHC677N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.18082&json=true","fetch_graph":"https://pith.science/api/pith-number/ZKHC677N4SW5HP2JU7VFVP4QD5/graph.json","fetch_events":"https://pith.science/api/pith-number/ZKHC677N4SW5HP2JU7VFVP4QD5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZKHC677N4SW5HP2JU7VFVP4QD5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZKHC677N4SW5HP2JU7VFVP4QD5/action/storage_attestation","attest_author":"https://pith.science/pith/ZKHC677N4SW5HP2JU7VFVP4QD5/action/author_attestation","sign_citation":"https://pith.science/pith/ZKHC677N4SW5HP2JU7VFVP4QD5/action/citation_signature","submit_replication":"https://pith.science/pith/ZKHC677N4SW5HP2JU7VFVP4QD5/action/replication_record"}},"created_at":"2026-07-05T10:38:02.286525+00:00","updated_at":"2026-07-05T10:38:02.286525+00:00"}