{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:27QPY4JD6GPZK5MQKEGCLWAYV4","short_pith_number":"pith:27QPY4JD","schema_version":"1.0","canonical_sha256":"d7e0fc7123f19f957590510c25d818af2caf952a1cce32e4d6f4f0a2cbe965d1","source":{"kind":"arxiv","id":"2501.14413","version":1},"attestation_state":"computed","paper":{"title":"Context-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Armstrong Aboah, Blessing Agyei Kyem, Joshua Kofi Asamoah","submitted_at":"2025-01-24T11:28:17Z","abstract_excerpt":"The accurate detection and segmentation of pavement distresses, particularly tiny and small cracks, are critical for early intervention and preventive maintenance in transportation infrastructure. Traditional manual inspection methods are labor-intensive and inconsistent, while existing deep learning models struggle with fine-grained segmentation and computational efficiency. To address these challenges, this study proposes Context-CrackNet, a novel encoder-decoder architecture featuring the Region-Focused Enhancement Module (RFEM) and Context-Aware Global Module (CAGM). These innovations enha"},"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":"2501.14413","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-24T11:28:17Z","cross_cats_sorted":[],"title_canon_sha256":"bcad2081e4243f5d8de0971b68282fb96d0ef4e274148cb1e02f604882fff7e1","abstract_canon_sha256":"4b8fa8c60d045fe5ee9f6316ced2bc8c131c199db1906911b621bf1a494fd0fa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:05:00.357223Z","signature_b64":"4IFN2wt5DPpjcelik0c9wTdmUSsp79B9tbTgINL1ogWy8pa9172tmxWPlnnCDDT8kaBzHPeQzaEL6226STBPBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7e0fc7123f19f957590510c25d818af2caf952a1cce32e4d6f4f0a2cbe965d1","last_reissued_at":"2026-07-05T10:05:00.356830Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:05:00.356830Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Context-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Armstrong Aboah, Blessing Agyei Kyem, Joshua Kofi Asamoah","submitted_at":"2025-01-24T11:28:17Z","abstract_excerpt":"The accurate detection and segmentation of pavement distresses, particularly tiny and small cracks, are critical for early intervention and preventive maintenance in transportation infrastructure. Traditional manual inspection methods are labor-intensive and inconsistent, while existing deep learning models struggle with fine-grained segmentation and computational efficiency. To address these challenges, this study proposes Context-CrackNet, a novel encoder-decoder architecture featuring the Region-Focused Enhancement Module (RFEM) and Context-Aware Global Module (CAGM). These innovations enha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14413","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/2501.14413/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":"2501.14413","created_at":"2026-07-05T10:05:00.356883+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.14413v1","created_at":"2026-07-05T10:05:00.356883+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14413","created_at":"2026-07-05T10:05:00.356883+00:00"},{"alias_kind":"pith_short_12","alias_value":"27QPY4JD6GPZ","created_at":"2026-07-05T10:05:00.356883+00:00"},{"alias_kind":"pith_short_16","alias_value":"27QPY4JD6GPZK5MQ","created_at":"2026-07-05T10:05:00.356883+00:00"},{"alias_kind":"pith_short_8","alias_value":"27QPY4JD","created_at":"2026-07-05T10:05:00.356883+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.12456","citing_title":"Demographics-Informed Neural Network for Multi-Modal Spatiotemporal forecasting of Urban Growth and Travel Patterns Using Satellite Imagery","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/27QPY4JD6GPZK5MQKEGCLWAYV4","json":"https://pith.science/pith/27QPY4JD6GPZK5MQKEGCLWAYV4.json","graph_json":"https://pith.science/api/pith-number/27QPY4JD6GPZK5MQKEGCLWAYV4/graph.json","events_json":"https://pith.science/api/pith-number/27QPY4JD6GPZK5MQKEGCLWAYV4/events.json","paper":"https://pith.science/paper/27QPY4JD"},"agent_actions":{"view_html":"https://pith.science/pith/27QPY4JD6GPZK5MQKEGCLWAYV4","download_json":"https://pith.science/pith/27QPY4JD6GPZK5MQKEGCLWAYV4.json","view_paper":"https://pith.science/paper/27QPY4JD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.14413&json=true","fetch_graph":"https://pith.science/api/pith-number/27QPY4JD6GPZK5MQKEGCLWAYV4/graph.json","fetch_events":"https://pith.science/api/pith-number/27QPY4JD6GPZK5MQKEGCLWAYV4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/27QPY4JD6GPZK5MQKEGCLWAYV4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/27QPY4JD6GPZK5MQKEGCLWAYV4/action/storage_attestation","attest_author":"https://pith.science/pith/27QPY4JD6GPZK5MQKEGCLWAYV4/action/author_attestation","sign_citation":"https://pith.science/pith/27QPY4JD6GPZK5MQKEGCLWAYV4/action/citation_signature","submit_replication":"https://pith.science/pith/27QPY4JD6GPZK5MQKEGCLWAYV4/action/replication_record"}},"created_at":"2026-07-05T10:05:00.356883+00:00","updated_at":"2026-07-05T10:05:00.356883+00:00"}