{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:EGX4HMAK5X7O2MS4T5PZZIPS7Y","short_pith_number":"pith:EGX4HMAK","schema_version":"1.0","canonical_sha256":"21afc3b00aedfeed325c9f5f9ca1f2fe023dc5b66f114485aeb85eb06f7684cf","source":{"kind":"arxiv","id":"2207.00589","version":1},"attestation_state":"computed","paper":{"title":"SSD-Faster Net: A Hybrid Network for Industrial Defect Inspection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Jingyao Wang, Naigong Yu","submitted_at":"2022-07-03T08:52:15Z","abstract_excerpt":"The quality of industrial components is critical to the production of special equipment such as robots. Defect inspection of these components is an efficient way to ensure quality. In this paper, we propose a hybrid network, SSD-Faster Net, for industrial defect inspection of rails, insulators, commutators etc. SSD-Faster Net is a two-stage network, including SSD for quickly locating defective blocks, and an improved Faster R-CNN for defect segmentation. For the former, we propose a novel slice localization mechanism to help SSD scan quickly. The second stage is based on improved Faster R-CNN,"},"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":"2207.00589","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-03T08:52:15Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"07d8c4dd9f2975675476c8fd892493bca1b8b296868dd9bd3ac006eb90748bd2","abstract_canon_sha256":"0af60ee95458ab7ac1ec8d67f65741715a1642da0c8a791f523dc79c5b2a5878"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:36:50.281568Z","signature_b64":"/NofA6IIL0kTP7RkYF2rZsPb+koFE+mYUlJextEK30Cgdf/xmI66yHQeOOsQbyaYnxn3UVg5hPMQxK7vZK51BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21afc3b00aedfeed325c9f5f9ca1f2fe023dc5b66f114485aeb85eb06f7684cf","last_reissued_at":"2026-07-05T04:36:50.281114Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:36:50.281114Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SSD-Faster Net: A Hybrid Network for Industrial Defect Inspection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Jingyao Wang, Naigong Yu","submitted_at":"2022-07-03T08:52:15Z","abstract_excerpt":"The quality of industrial components is critical to the production of special equipment such as robots. Defect inspection of these components is an efficient way to ensure quality. In this paper, we propose a hybrid network, SSD-Faster Net, for industrial defect inspection of rails, insulators, commutators etc. SSD-Faster Net is a two-stage network, including SSD for quickly locating defective blocks, and an improved Faster R-CNN for defect segmentation. For the former, we propose a novel slice localization mechanism to help SSD scan quickly. The second stage is based on improved Faster R-CNN,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.00589","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/2207.00589/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":"2207.00589","created_at":"2026-07-05T04:36:50.281169+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.00589v1","created_at":"2026-07-05T04:36:50.281169+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.00589","created_at":"2026-07-05T04:36:50.281169+00:00"},{"alias_kind":"pith_short_12","alias_value":"EGX4HMAK5X7O","created_at":"2026-07-05T04:36:50.281169+00:00"},{"alias_kind":"pith_short_16","alias_value":"EGX4HMAK5X7O2MS4","created_at":"2026-07-05T04:36:50.281169+00:00"},{"alias_kind":"pith_short_8","alias_value":"EGX4HMAK","created_at":"2026-07-05T04:36:50.281169+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.02905","citing_title":"UniSpector: Towards Universal Open-set Defect Recognition via Spectral-Contrastive Visual Prompting","ref_index":29,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EGX4HMAK5X7O2MS4T5PZZIPS7Y","json":"https://pith.science/pith/EGX4HMAK5X7O2MS4T5PZZIPS7Y.json","graph_json":"https://pith.science/api/pith-number/EGX4HMAK5X7O2MS4T5PZZIPS7Y/graph.json","events_json":"https://pith.science/api/pith-number/EGX4HMAK5X7O2MS4T5PZZIPS7Y/events.json","paper":"https://pith.science/paper/EGX4HMAK"},"agent_actions":{"view_html":"https://pith.science/pith/EGX4HMAK5X7O2MS4T5PZZIPS7Y","download_json":"https://pith.science/pith/EGX4HMAK5X7O2MS4T5PZZIPS7Y.json","view_paper":"https://pith.science/paper/EGX4HMAK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.00589&json=true","fetch_graph":"https://pith.science/api/pith-number/EGX4HMAK5X7O2MS4T5PZZIPS7Y/graph.json","fetch_events":"https://pith.science/api/pith-number/EGX4HMAK5X7O2MS4T5PZZIPS7Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EGX4HMAK5X7O2MS4T5PZZIPS7Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EGX4HMAK5X7O2MS4T5PZZIPS7Y/action/storage_attestation","attest_author":"https://pith.science/pith/EGX4HMAK5X7O2MS4T5PZZIPS7Y/action/author_attestation","sign_citation":"https://pith.science/pith/EGX4HMAK5X7O2MS4T5PZZIPS7Y/action/citation_signature","submit_replication":"https://pith.science/pith/EGX4HMAK5X7O2MS4T5PZZIPS7Y/action/replication_record"}},"created_at":"2026-07-05T04:36:50.281169+00:00","updated_at":"2026-07-05T04:36:50.281169+00:00"}