{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:5QQ6MTQTHCFT463J3XOXWZ7HBB","short_pith_number":"pith:5QQ6MTQT","schema_version":"1.0","canonical_sha256":"ec21e64e13388b3e7b69dddd7b67e7087c00561cc910db9ec250544a0a65a6c9","source":{"kind":"arxiv","id":"2105.10892","version":1},"attestation_state":"computed","paper":{"title":"Fast Crack Detection Using Convolutional Neural Network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Fangzheng Lin, Jiesheng Yang, Peter Katranuschkov, Raimar J. Scherer, Yusheng Xiang","submitted_at":"2021-05-23T09:13:42Z","abstract_excerpt":"To improve the efficiency and reduce the labour cost of the renovation process, this study presents a lightweight Convolutional Neural Network (CNN)-based architecture to extract crack-like features, such as cracks and joints. Moreover, Transfer Learning (TF) method was used to save training time while offering comparable prediction results. For three different objectives: 1) Detection of the concrete cracks; 2) Detection of natural stone cracks; 3) Differentiation between joints and cracks in natural stone; We built a natural stone dataset with joints and cracks information as complementary f"},"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":"2105.10892","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-05-23T09:13:42Z","cross_cats_sorted":[],"title_canon_sha256":"1b6d3acfd30e5b8e3aed22d0cce8ff11d38d9c676740514cfda29c57e8d08294","abstract_canon_sha256":"b2f1b4bee8c4434a45398f9f1d1e17bca366381bbf9f02ab70bb5af79d9edf1a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:42:33.193275Z","signature_b64":"lH9eiJEeJntg6FWrY4ruqOCG/C/GDc1I8xacSwpTFWbnELBD0Jarp/nXQ2H+jC0HUE+povTLrz7M8jMbQ90XBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ec21e64e13388b3e7b69dddd7b67e7087c00561cc910db9ec250544a0a65a6c9","last_reissued_at":"2026-07-05T02:42:33.192781Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:42:33.192781Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast Crack Detection Using Convolutional Neural Network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Fangzheng Lin, Jiesheng Yang, Peter Katranuschkov, Raimar J. Scherer, Yusheng Xiang","submitted_at":"2021-05-23T09:13:42Z","abstract_excerpt":"To improve the efficiency and reduce the labour cost of the renovation process, this study presents a lightweight Convolutional Neural Network (CNN)-based architecture to extract crack-like features, such as cracks and joints. Moreover, Transfer Learning (TF) method was used to save training time while offering comparable prediction results. For three different objectives: 1) Detection of the concrete cracks; 2) Detection of natural stone cracks; 3) Differentiation between joints and cracks in natural stone; We built a natural stone dataset with joints and cracks information as complementary f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.10892","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/2105.10892/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":"2105.10892","created_at":"2026-07-05T02:42:33.192842+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.10892v1","created_at":"2026-07-05T02:42:33.192842+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.10892","created_at":"2026-07-05T02:42:33.192842+00:00"},{"alias_kind":"pith_short_12","alias_value":"5QQ6MTQTHCFT","created_at":"2026-07-05T02:42:33.192842+00:00"},{"alias_kind":"pith_short_16","alias_value":"5QQ6MTQTHCFT463J","created_at":"2026-07-05T02:42:33.192842+00:00"},{"alias_kind":"pith_short_8","alias_value":"5QQ6MTQT","created_at":"2026-07-05T02:42:33.192842+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/5QQ6MTQTHCFT463J3XOXWZ7HBB","json":"https://pith.science/pith/5QQ6MTQTHCFT463J3XOXWZ7HBB.json","graph_json":"https://pith.science/api/pith-number/5QQ6MTQTHCFT463J3XOXWZ7HBB/graph.json","events_json":"https://pith.science/api/pith-number/5QQ6MTQTHCFT463J3XOXWZ7HBB/events.json","paper":"https://pith.science/paper/5QQ6MTQT"},"agent_actions":{"view_html":"https://pith.science/pith/5QQ6MTQTHCFT463J3XOXWZ7HBB","download_json":"https://pith.science/pith/5QQ6MTQTHCFT463J3XOXWZ7HBB.json","view_paper":"https://pith.science/paper/5QQ6MTQT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.10892&json=true","fetch_graph":"https://pith.science/api/pith-number/5QQ6MTQTHCFT463J3XOXWZ7HBB/graph.json","fetch_events":"https://pith.science/api/pith-number/5QQ6MTQTHCFT463J3XOXWZ7HBB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5QQ6MTQTHCFT463J3XOXWZ7HBB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5QQ6MTQTHCFT463J3XOXWZ7HBB/action/storage_attestation","attest_author":"https://pith.science/pith/5QQ6MTQTHCFT463J3XOXWZ7HBB/action/author_attestation","sign_citation":"https://pith.science/pith/5QQ6MTQTHCFT463J3XOXWZ7HBB/action/citation_signature","submit_replication":"https://pith.science/pith/5QQ6MTQTHCFT463J3XOXWZ7HBB/action/replication_record"}},"created_at":"2026-07-05T02:42:33.192842+00:00","updated_at":"2026-07-05T02:42:33.192842+00:00"}