{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:P6JBH3OCVVRH53ZTYQKIDCEQC7","short_pith_number":"pith:P6JBH3OC","schema_version":"1.0","canonical_sha256":"7f9213edc2ad627eef33c41481889017f6711f94df543362b99ab00cde237d23","source":{"kind":"arxiv","id":"1805.02523","version":3},"attestation_state":"computed","paper":{"title":"Detecting Traffic Lights by Single Shot Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Julian M\\\"uller, Klaus Dietmayer","submitted_at":"2018-05-07T13:37:17Z","abstract_excerpt":"Recent improvements in object detection are driven by the success of convolutional neural networks (CNN). They are able to learn rich features outperforming hand-crafted features. So far, research in traffic light detection mainly focused on hand-crafted features, such as color, shape or brightness of the traffic light bulb. This paper presents a deep learning approach for accurate traffic light detection in adapting a single shot detection (SSD) approach. SSD performs object proposals creation and classification using a single CNN. The original SSD struggles in detecting very small objects, w"},"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":"1805.02523","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-07T13:37:17Z","cross_cats_sorted":[],"title_canon_sha256":"d4eb9019b885a46d2e38c4c2806fbd223804918d878dcaf0fe1de4711049f6fd","abstract_canon_sha256":"3879469b46675a17b3fe663f3722142066ee0e9ef4cdeed4c4e5b2d3684531d3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:03:36.919589Z","signature_b64":"3vNikDtMDbvVq8FTKvWzIJmcgYOCd9xct+Kfy85EyPGIkakxsNi24uVjHd4VHi0FeZriOZxsx4WEvRs3Ge8vAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f9213edc2ad627eef33c41481889017f6711f94df543362b99ab00cde237d23","last_reissued_at":"2026-05-18T00:03:36.919049Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:03:36.919049Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Detecting Traffic Lights by Single Shot Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Julian M\\\"uller, Klaus Dietmayer","submitted_at":"2018-05-07T13:37:17Z","abstract_excerpt":"Recent improvements in object detection are driven by the success of convolutional neural networks (CNN). They are able to learn rich features outperforming hand-crafted features. So far, research in traffic light detection mainly focused on hand-crafted features, such as color, shape or brightness of the traffic light bulb. This paper presents a deep learning approach for accurate traffic light detection in adapting a single shot detection (SSD) approach. SSD performs object proposals creation and classification using a single CNN. The original SSD struggles in detecting very small objects, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.02523","kind":"arxiv","version":3},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1805.02523","created_at":"2026-05-18T00:03:36.919129+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.02523v3","created_at":"2026-05-18T00:03:36.919129+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.02523","created_at":"2026-05-18T00:03:36.919129+00:00"},{"alias_kind":"pith_short_12","alias_value":"P6JBH3OCVVRH","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_16","alias_value":"P6JBH3OCVVRH53ZT","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_8","alias_value":"P6JBH3OC","created_at":"2026-05-18T12:32:43.782077+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/P6JBH3OCVVRH53ZTYQKIDCEQC7","json":"https://pith.science/pith/P6JBH3OCVVRH53ZTYQKIDCEQC7.json","graph_json":"https://pith.science/api/pith-number/P6JBH3OCVVRH53ZTYQKIDCEQC7/graph.json","events_json":"https://pith.science/api/pith-number/P6JBH3OCVVRH53ZTYQKIDCEQC7/events.json","paper":"https://pith.science/paper/P6JBH3OC"},"agent_actions":{"view_html":"https://pith.science/pith/P6JBH3OCVVRH53ZTYQKIDCEQC7","download_json":"https://pith.science/pith/P6JBH3OCVVRH53ZTYQKIDCEQC7.json","view_paper":"https://pith.science/paper/P6JBH3OC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.02523&json=true","fetch_graph":"https://pith.science/api/pith-number/P6JBH3OCVVRH53ZTYQKIDCEQC7/graph.json","fetch_events":"https://pith.science/api/pith-number/P6JBH3OCVVRH53ZTYQKIDCEQC7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P6JBH3OCVVRH53ZTYQKIDCEQC7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P6JBH3OCVVRH53ZTYQKIDCEQC7/action/storage_attestation","attest_author":"https://pith.science/pith/P6JBH3OCVVRH53ZTYQKIDCEQC7/action/author_attestation","sign_citation":"https://pith.science/pith/P6JBH3OCVVRH53ZTYQKIDCEQC7/action/citation_signature","submit_replication":"https://pith.science/pith/P6JBH3OCVVRH53ZTYQKIDCEQC7/action/replication_record"}},"created_at":"2026-05-18T00:03:36.919129+00:00","updated_at":"2026-05-18T00:03:36.919129+00:00"}