{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:Y2KRYBIYHB7LONHNBEVM75BGQQ","short_pith_number":"pith:Y2KRYBIY","schema_version":"1.0","canonical_sha256":"c6951c0518387eb734ed092acff4268423aa6a01dd1f8176841e10fca251890d","source":{"kind":"arxiv","id":"2506.21135","version":1},"attestation_state":"computed","paper":{"title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiawei Hu","submitted_at":"2025-06-26T10:32:37Z","abstract_excerpt":"Surface defect detection in industrial scenarios is both crucial and technically demanding due to the wide variability in defect types, irregular shapes and sizes, fine-grained requirements, and complex material textures. Although recent advances in AI-based detectors have improved performance, existing methods often suffer from redundant features, limited detail sensitivity, and weak robustness under multiscale conditions. To address these challenges, we propose YOLO-FDA, a novel YOLO-based detection framework that integrates fine-grained detail enhancement and attention-guided feature fusion"},"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":"2506.21135","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-26T10:32:37Z","cross_cats_sorted":[],"title_canon_sha256":"91f8db637e32a42a7b25704975ef15a2dee3bfee3d28acf10f66b0e0e3f8dcba","abstract_canon_sha256":"860be5d34ad30ba8f4ed94d15995fdb36724edbeae90c1a119e897fcfd12c4bd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:27:40.426839Z","signature_b64":"JcBFrokJtHH8G/e3dlhG7eOfwSfxZMPBR8Vi/xximTg85MtraTFaE1EKI1sygrOVYW7WaADjk7vcOKdEvX9IBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6951c0518387eb734ed092acff4268423aa6a01dd1f8176841e10fca251890d","last_reissued_at":"2026-07-05T11:27:40.426377Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:27:40.426377Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiawei Hu","submitted_at":"2025-06-26T10:32:37Z","abstract_excerpt":"Surface defect detection in industrial scenarios is both crucial and technically demanding due to the wide variability in defect types, irregular shapes and sizes, fine-grained requirements, and complex material textures. Although recent advances in AI-based detectors have improved performance, existing methods often suffer from redundant features, limited detail sensitivity, and weak robustness under multiscale conditions. To address these challenges, we propose YOLO-FDA, a novel YOLO-based detection framework that integrates fine-grained detail enhancement and attention-guided feature fusion"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.21135","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/2506.21135/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":"2506.21135","created_at":"2026-07-05T11:27:40.426442+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.21135v1","created_at":"2026-07-05T11:27:40.426442+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.21135","created_at":"2026-07-05T11:27:40.426442+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y2KRYBIYHB7L","created_at":"2026-07-05T11:27:40.426442+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y2KRYBIYHB7LONHN","created_at":"2026-07-05T11:27:40.426442+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y2KRYBIY","created_at":"2026-07-05T11:27:40.426442+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/Y2KRYBIYHB7LONHNBEVM75BGQQ","json":"https://pith.science/pith/Y2KRYBIYHB7LONHNBEVM75BGQQ.json","graph_json":"https://pith.science/api/pith-number/Y2KRYBIYHB7LONHNBEVM75BGQQ/graph.json","events_json":"https://pith.science/api/pith-number/Y2KRYBIYHB7LONHNBEVM75BGQQ/events.json","paper":"https://pith.science/paper/Y2KRYBIY"},"agent_actions":{"view_html":"https://pith.science/pith/Y2KRYBIYHB7LONHNBEVM75BGQQ","download_json":"https://pith.science/pith/Y2KRYBIYHB7LONHNBEVM75BGQQ.json","view_paper":"https://pith.science/paper/Y2KRYBIY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.21135&json=true","fetch_graph":"https://pith.science/api/pith-number/Y2KRYBIYHB7LONHNBEVM75BGQQ/graph.json","fetch_events":"https://pith.science/api/pith-number/Y2KRYBIYHB7LONHNBEVM75BGQQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y2KRYBIYHB7LONHNBEVM75BGQQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y2KRYBIYHB7LONHNBEVM75BGQQ/action/storage_attestation","attest_author":"https://pith.science/pith/Y2KRYBIYHB7LONHNBEVM75BGQQ/action/author_attestation","sign_citation":"https://pith.science/pith/Y2KRYBIYHB7LONHNBEVM75BGQQ/action/citation_signature","submit_replication":"https://pith.science/pith/Y2KRYBIYHB7LONHNBEVM75BGQQ/action/replication_record"}},"created_at":"2026-07-05T11:27:40.426442+00:00","updated_at":"2026-07-05T11:27:40.426442+00:00"}