{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:DUNHCLIOUS65O6ZBITOZC67FN3","short_pith_number":"pith:DUNHCLIO","schema_version":"1.0","canonical_sha256":"1d1a712d0ea4bdd77b2144dd917be56ec67cef35b9df766304143be0adaec657","source":{"kind":"arxiv","id":"2004.12337","version":2},"attestation_state":"computed","paper":{"title":"KrakN: Transfer Learning framework for thin crack detection in infrastructure maintenance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.CV","authors_text":"Bartosz W\\'ojcik, Jaros{\\l}aw Adam Miszczak, Mateusz \\.Zarski","submitted_at":"2020-04-26T09:57:36Z","abstract_excerpt":"Monitoring the technical condition of infrastructure is a crucial element to its maintenance. Currently applied methods are outdated, labour-intensive and inaccurate. At the same time, the latest methods using Artificial Intelligence techniques are severely limited in their application due to two main factors -- labour-intensive gathering of new datasets and high demand for computing power. We propose to utilize custom made framework -- KrakN, to overcome these limiting factors. It enables the development of unique infrastructure defects detectors on digital images, achieving the accuracy of a"},"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":"2004.12337","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-04-26T09:57:36Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"e3071eb117fb3898bf87b4c7a3aa73d844fc978763defa9068090a6f082d5c59","abstract_canon_sha256":"cfea7804f3a3dd6fe8e1200beb0cbbae102db1e43345b3535736e3f5359f222e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:35:47.908384Z","signature_b64":"65RiyUEOrRv71SS9/+QHz9ikK0HhDn7FeewqSPIA01ddxk6SL2aTte/4YSD5yFdZba904nlU/zoLVz2fHQHeDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1d1a712d0ea4bdd77b2144dd917be56ec67cef35b9df766304143be0adaec657","last_reissued_at":"2026-07-05T03:35:47.908022Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:35:47.908022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"KrakN: Transfer Learning framework for thin crack detection in infrastructure maintenance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.CV","authors_text":"Bartosz W\\'ojcik, Jaros{\\l}aw Adam Miszczak, Mateusz \\.Zarski","submitted_at":"2020-04-26T09:57:36Z","abstract_excerpt":"Monitoring the technical condition of infrastructure is a crucial element to its maintenance. Currently applied methods are outdated, labour-intensive and inaccurate. At the same time, the latest methods using Artificial Intelligence techniques are severely limited in their application due to two main factors -- labour-intensive gathering of new datasets and high demand for computing power. We propose to utilize custom made framework -- KrakN, to overcome these limiting factors. It enables the development of unique infrastructure defects detectors on digital images, achieving the accuracy of a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.12337","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/2004.12337/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":"2004.12337","created_at":"2026-07-05T03:35:47.908071+00:00"},{"alias_kind":"arxiv_version","alias_value":"2004.12337v2","created_at":"2026-07-05T03:35:47.908071+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.12337","created_at":"2026-07-05T03:35:47.908071+00:00"},{"alias_kind":"pith_short_12","alias_value":"DUNHCLIOUS65","created_at":"2026-07-05T03:35:47.908071+00:00"},{"alias_kind":"pith_short_16","alias_value":"DUNHCLIOUS65O6ZB","created_at":"2026-07-05T03:35:47.908071+00:00"},{"alias_kind":"pith_short_8","alias_value":"DUNHCLIO","created_at":"2026-07-05T03:35:47.908071+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/DUNHCLIOUS65O6ZBITOZC67FN3","json":"https://pith.science/pith/DUNHCLIOUS65O6ZBITOZC67FN3.json","graph_json":"https://pith.science/api/pith-number/DUNHCLIOUS65O6ZBITOZC67FN3/graph.json","events_json":"https://pith.science/api/pith-number/DUNHCLIOUS65O6ZBITOZC67FN3/events.json","paper":"https://pith.science/paper/DUNHCLIO"},"agent_actions":{"view_html":"https://pith.science/pith/DUNHCLIOUS65O6ZBITOZC67FN3","download_json":"https://pith.science/pith/DUNHCLIOUS65O6ZBITOZC67FN3.json","view_paper":"https://pith.science/paper/DUNHCLIO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2004.12337&json=true","fetch_graph":"https://pith.science/api/pith-number/DUNHCLIOUS65O6ZBITOZC67FN3/graph.json","fetch_events":"https://pith.science/api/pith-number/DUNHCLIOUS65O6ZBITOZC67FN3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DUNHCLIOUS65O6ZBITOZC67FN3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DUNHCLIOUS65O6ZBITOZC67FN3/action/storage_attestation","attest_author":"https://pith.science/pith/DUNHCLIOUS65O6ZBITOZC67FN3/action/author_attestation","sign_citation":"https://pith.science/pith/DUNHCLIOUS65O6ZBITOZC67FN3/action/citation_signature","submit_replication":"https://pith.science/pith/DUNHCLIOUS65O6ZBITOZC67FN3/action/replication_record"}},"created_at":"2026-07-05T03:35:47.908071+00:00","updated_at":"2026-07-05T03:35:47.908071+00:00"}