{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NGI4I5UD65KJ7UGUAUZOLYUDEW","short_pith_number":"pith:NGI4I5UD","schema_version":"1.0","canonical_sha256":"6991c47683f7549fd0d40532e5e28325bdbbb189e744818ca88bcdb5920879ec","source":{"kind":"arxiv","id":"2507.19912","version":4},"attestation_state":"computed","paper":{"title":"DriveIndia: An Object Detection Dataset for Diverse Indian Traffic Scenes","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"D. Santhosh Reddy, P. Rajalakshmi, Rishav Kumar","submitted_at":"2025-07-26T10:52:03Z","abstract_excerpt":"We introduce DriveIndia, a large-scale object detection dataset purpose-built to capture the complexity and unpredictability of Indian traffic environments. The dataset contains 66,986 high-resolution images annotated in YOLO format across 24 traffic-relevant object categories, encompassing diverse conditions such as varied weather (fog, rain), illumination changes, heterogeneous road infrastructure, and dense, mixed traffic patterns and collected over 120+ hours and covering 3,400+ kilometers across urban, rural, and highway routes. DriveIndia offers a comprehensive benchmark for real-world a"},"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":"2507.19912","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-26T10:52:03Z","cross_cats_sorted":[],"title_canon_sha256":"25a05654f891694a75d34b022733e2a9f9914f71d4ee78497122e8fe9b1efc75","abstract_canon_sha256":"f34aca028302d897f902d8d7f7fb9ef151376c5f2cf29a0732b88e0c4815a4be"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:59:18.302143Z","signature_b64":"3O10eDkEmuug6s3XSvCFusF02qyMmsDB+7N/raDzkpeKaPAv3aaH2kP6m+cdlxE+762glyYyGbE6L6BTH6NJCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6991c47683f7549fd0d40532e5e28325bdbbb189e744818ca88bcdb5920879ec","last_reissued_at":"2026-07-05T11:59:18.301530Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:59:18.301530Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DriveIndia: An Object Detection Dataset for Diverse Indian Traffic Scenes","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"D. Santhosh Reddy, P. Rajalakshmi, Rishav Kumar","submitted_at":"2025-07-26T10:52:03Z","abstract_excerpt":"We introduce DriveIndia, a large-scale object detection dataset purpose-built to capture the complexity and unpredictability of Indian traffic environments. The dataset contains 66,986 high-resolution images annotated in YOLO format across 24 traffic-relevant object categories, encompassing diverse conditions such as varied weather (fog, rain), illumination changes, heterogeneous road infrastructure, and dense, mixed traffic patterns and collected over 120+ hours and covering 3,400+ kilometers across urban, rural, and highway routes. DriveIndia offers a comprehensive benchmark for real-world a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.19912","kind":"arxiv","version":4},"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/2507.19912/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":"2507.19912","created_at":"2026-07-05T11:59:18.301655+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.19912v4","created_at":"2026-07-05T11:59:18.301655+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.19912","created_at":"2026-07-05T11:59:18.301655+00:00"},{"alias_kind":"pith_short_12","alias_value":"NGI4I5UD65KJ","created_at":"2026-07-05T11:59:18.301655+00:00"},{"alias_kind":"pith_short_16","alias_value":"NGI4I5UD65KJ7UGU","created_at":"2026-07-05T11:59:18.301655+00:00"},{"alias_kind":"pith_short_8","alias_value":"NGI4I5UD","created_at":"2026-07-05T11:59:18.301655+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12066","citing_title":"Performance Analysis of YOLOv11 and YOLOv8 for Mixed Traffic Object Detection under Adverse Weather Conditions in Developing Countries","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2606.07626","citing_title":"Eyes All Around: Design and Analysis of 360-Degree LiDAR Perception Using Equivariant Feature Learning in Unstructured Traffic","ref_index":14,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NGI4I5UD65KJ7UGUAUZOLYUDEW","json":"https://pith.science/pith/NGI4I5UD65KJ7UGUAUZOLYUDEW.json","graph_json":"https://pith.science/api/pith-number/NGI4I5UD65KJ7UGUAUZOLYUDEW/graph.json","events_json":"https://pith.science/api/pith-number/NGI4I5UD65KJ7UGUAUZOLYUDEW/events.json","paper":"https://pith.science/paper/NGI4I5UD"},"agent_actions":{"view_html":"https://pith.science/pith/NGI4I5UD65KJ7UGUAUZOLYUDEW","download_json":"https://pith.science/pith/NGI4I5UD65KJ7UGUAUZOLYUDEW.json","view_paper":"https://pith.science/paper/NGI4I5UD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.19912&json=true","fetch_graph":"https://pith.science/api/pith-number/NGI4I5UD65KJ7UGUAUZOLYUDEW/graph.json","fetch_events":"https://pith.science/api/pith-number/NGI4I5UD65KJ7UGUAUZOLYUDEW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NGI4I5UD65KJ7UGUAUZOLYUDEW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NGI4I5UD65KJ7UGUAUZOLYUDEW/action/storage_attestation","attest_author":"https://pith.science/pith/NGI4I5UD65KJ7UGUAUZOLYUDEW/action/author_attestation","sign_citation":"https://pith.science/pith/NGI4I5UD65KJ7UGUAUZOLYUDEW/action/citation_signature","submit_replication":"https://pith.science/pith/NGI4I5UD65KJ7UGUAUZOLYUDEW/action/replication_record"}},"created_at":"2026-07-05T11:59:18.301655+00:00","updated_at":"2026-07-05T11:59:18.301655+00:00"}