{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:UUKJDTYTC73ZUB7J7O3X5CZPC2","short_pith_number":"pith:UUKJDTYT","canonical_record":{"source":{"id":"1803.01114","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-03T06:18:27Z","cross_cats_sorted":[],"title_canon_sha256":"86f3d5356fd40881476eae069ca741d1a6d4b10fff3744e123400d0a58e0cbdc","abstract_canon_sha256":"86251233cc9903d8d6f60d4a99be370332c6cdc95c32483adcc9c1526b257f92"},"schema_version":"1.0"},"canonical_sha256":"a51491cf1317f79a07e9fbb77e8b2f16bc14ff31905c249605be339d9b9db726","source":{"kind":"arxiv","id":"1803.01114","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.01114","created_at":"2026-05-18T00:22:01Z"},{"alias_kind":"arxiv_version","alias_value":"1803.01114v1","created_at":"2026-05-18T00:22:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.01114","created_at":"2026-05-18T00:22:01Z"},{"alias_kind":"pith_short_12","alias_value":"UUKJDTYTC73Z","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"UUKJDTYTC73ZUB7J","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"UUKJDTYT","created_at":"2026-05-18T12:32:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:UUKJDTYTC73ZUB7J7O3X5CZPC2","target":"record","payload":{"canonical_record":{"source":{"id":"1803.01114","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-03T06:18:27Z","cross_cats_sorted":[],"title_canon_sha256":"86f3d5356fd40881476eae069ca741d1a6d4b10fff3744e123400d0a58e0cbdc","abstract_canon_sha256":"86251233cc9903d8d6f60d4a99be370332c6cdc95c32483adcc9c1526b257f92"},"schema_version":"1.0"},"canonical_sha256":"a51491cf1317f79a07e9fbb77e8b2f16bc14ff31905c249605be339d9b9db726","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:01.735421Z","signature_b64":"Ee6DLxK7beXIp3e0JDxw/V3a1kRJOhPPI4q25LQyAikrPtf44P60haNOYFOucqCG1eTZ2Is1e/QdXX7B5lxfCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a51491cf1317f79a07e9fbb77e8b2f16bc14ff31905c249605be339d9b9db726","last_reissued_at":"2026-05-18T00:22:01.734850Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:01.734850Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.01114","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:22:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+4XW2gjcfvw9Fh3yIC/lPDRheOmJqNpW4ZrcZGv/8tR/spSoKkS7BuBUGDtcVnQym15qnlOY28el0E4PMc36Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T20:43:46.338368Z"},"content_sha256":"9a78c22de1559ff0c386b3d32c0d9149ff30e0c4405732115a687078015eded6","schema_version":"1.0","event_id":"sha256:9a78c22de1559ff0c386b3d32c0d9149ff30e0c4405732115a687078015eded6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:UUKJDTYTC73ZUB7J7O3X5CZPC2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Focal Loss Dense Detector for Vehicle Surveillance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Benedict Uzochukwu, Peng Cheng, Xiaoliang Wang, Xinchuan Liu","submitted_at":"2018-03-03T06:18:27Z","abstract_excerpt":"Deep learning has been widely recognized as a promising approach in different computer vision applications. Specifically, one-stage object detector and two-stage object detector are regarded as the most important two groups of Convolutional Neural Network based object detection methods. One-stage object detector could usually outperform two-stage object detector in speed; However, it normally trails in detection accuracy, compared with two-stage object detectors. In this study, focal loss based RetinaNet, which works as one-stage object detector, is utilized to be able to well match the speed "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.01114","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:22:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tVB5RKyVSG+QR463nkC9+Gy10ds6gcnJnj0ufeoEosDYHvoKfAQkhs/psm61QwLRPdpKrUXQK1WM+/7UeQdXDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T20:43:46.338992Z"},"content_sha256":"57119c14d256ea6ade133d695855aa57c180222ba2bf69d054c1510d6a043133","schema_version":"1.0","event_id":"sha256:57119c14d256ea6ade133d695855aa57c180222ba2bf69d054c1510d6a043133"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UUKJDTYTC73ZUB7J7O3X5CZPC2/bundle.json","state_url":"https://pith.science/pith/UUKJDTYTC73ZUB7J7O3X5CZPC2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UUKJDTYTC73ZUB7J7O3X5CZPC2/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-28T20:43:46Z","links":{"resolver":"https://pith.science/pith/UUKJDTYTC73ZUB7J7O3X5CZPC2","bundle":"https://pith.science/pith/UUKJDTYTC73ZUB7J7O3X5CZPC2/bundle.json","state":"https://pith.science/pith/UUKJDTYTC73ZUB7J7O3X5CZPC2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UUKJDTYTC73ZUB7J7O3X5CZPC2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:UUKJDTYTC73ZUB7J7O3X5CZPC2","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"86251233cc9903d8d6f60d4a99be370332c6cdc95c32483adcc9c1526b257f92","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-03T06:18:27Z","title_canon_sha256":"86f3d5356fd40881476eae069ca741d1a6d4b10fff3744e123400d0a58e0cbdc"},"schema_version":"1.0","source":{"id":"1803.01114","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.01114","created_at":"2026-05-18T00:22:01Z"},{"alias_kind":"arxiv_version","alias_value":"1803.01114v1","created_at":"2026-05-18T00:22:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.01114","created_at":"2026-05-18T00:22:01Z"},{"alias_kind":"pith_short_12","alias_value":"UUKJDTYTC73Z","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"UUKJDTYTC73ZUB7J","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"UUKJDTYT","created_at":"2026-05-18T12:32:56Z"}],"graph_snapshots":[{"event_id":"sha256:57119c14d256ea6ade133d695855aa57c180222ba2bf69d054c1510d6a043133","target":"graph","created_at":"2026-05-18T00:22:01Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Deep learning has been widely recognized as a promising approach in different computer vision applications. Specifically, one-stage object detector and two-stage object detector are regarded as the most important two groups of Convolutional Neural Network based object detection methods. One-stage object detector could usually outperform two-stage object detector in speed; However, it normally trails in detection accuracy, compared with two-stage object detectors. In this study, focal loss based RetinaNet, which works as one-stage object detector, is utilized to be able to well match the speed ","authors_text":"Benedict Uzochukwu, Peng Cheng, Xiaoliang Wang, Xinchuan Liu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-03T06:18:27Z","title":"Focal Loss Dense Detector for Vehicle Surveillance"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.01114","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9a78c22de1559ff0c386b3d32c0d9149ff30e0c4405732115a687078015eded6","target":"record","created_at":"2026-05-18T00:22:01Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"86251233cc9903d8d6f60d4a99be370332c6cdc95c32483adcc9c1526b257f92","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-03T06:18:27Z","title_canon_sha256":"86f3d5356fd40881476eae069ca741d1a6d4b10fff3744e123400d0a58e0cbdc"},"schema_version":"1.0","source":{"id":"1803.01114","kind":"arxiv","version":1}},"canonical_sha256":"a51491cf1317f79a07e9fbb77e8b2f16bc14ff31905c249605be339d9b9db726","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a51491cf1317f79a07e9fbb77e8b2f16bc14ff31905c249605be339d9b9db726","first_computed_at":"2026-05-18T00:22:01.734850Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:22:01.734850Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ee6DLxK7beXIp3e0JDxw/V3a1kRJOhPPI4q25LQyAikrPtf44P60haNOYFOucqCG1eTZ2Is1e/QdXX7B5lxfCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:22:01.735421Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.01114","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9a78c22de1559ff0c386b3d32c0d9149ff30e0c4405732115a687078015eded6","sha256:57119c14d256ea6ade133d695855aa57c180222ba2bf69d054c1510d6a043133"],"state_sha256":"5ae49ddfb7e6b6c433d53a5108b758e033cc8f6f8d9fbb41fe57d4b7e69b6e3d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nvjE35Y6Oa4OAZhyDE8Ehkoz/6c/NzoYw0YNy4FkDWuux1tMjQYDdxMNUC2jbm4mKbxLLlT85abcFBgzhAExCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T20:43:46.342218Z","bundle_sha256":"97d2bf3d7bcd06529c54efd7f918ca1b34d0c85514fbbf6049fe1a3fc5a0e34d"}}