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Paper Citation Record · LEDGER

Efficient Edge Deployment of Quantized YOLOv4-Tiny for Aerial Emergency Object Detection on Raspberry Pi 5

As of 7 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 0 inbound Pith citation observations for arXiv:2506.09300.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.09300 v1

Coverage vector

measured 6 of 6 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:55:17.206707Z

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

6 of 6 outbound references displayed

  • verified exact0
  • verified fuzzy3
  • unresolved3
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 700b1e57-85cf-4837-a9f3-7847eb003716 · outbound

This paper cites You only look once: Unified, real-time object detection,.

Efficient Edge Deployment of Quantized YOLOv4-Tiny for Aerial Emergency Object Detection on Raspberry Pi 5 You only look once: Unified, real-time object detection,

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T04:55:16.605588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:55:16.605588Z digest=sha256:5acbe5eb21df62908772f1c9ab99d6d18f5b26de0bdda9b28e5efcf81ac0bd0e

Observation afccb42e-06cc-4c1b-8a55-558080d8b0f8 · outbound

This paper cites YOLOv4: Optimal Speed and Accuracy of Object Detection.

Efficient Edge Deployment of Quantized YOLOv4-Tiny for Aerial Emergency Object Detection on Raspberry Pi 5 YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-07T04:55:16.701190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:55:16.701190Z digest=sha256:b2e2322dcb6e494ef1bde3a4d6943a95174557e6a838325e47323ffa2e4677c5

Observation 31187274-44e9-4dbd-bd5c-fce1619482d0 · outbound

This paper cites SSD: Single shot multibox detector,.

Efficient Edge Deployment of Quantized YOLOv4-Tiny for Aerial Emergency Object Detection on Raspberry Pi 5 SSD: Single shot multibox detector,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:55:17.872254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:55:16.810762Z digest=sha256:43ffd4b6b8e5973ab4d424c8d71ca44b5f1794ba71f8fe5b019544c9bf7b1eef

Observation 3b9dd624-010a-4845-9c4c-4a2dda3ad7e6 · outbound

This paper cites Ten- sorFlow: A system for large-scale machine learning,.

Efficient Edge Deployment of Quantized YOLOv4-Tiny for Aerial Emergency Object Detection on Raspberry Pi 5 Ten- sorFlow: A system for large-scale machine learning,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:55:17.611343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:55:16.894107Z digest=sha256:678ca6c60bf5fd871eeae5618947859c07b9b5b864bc9cc780fcecda7f3412bf

Observation 1d9aacb5-0d68-41ea-b992-0e0ca63b59ad · outbound

This paper cites Focal loss for dense object detection,.

Efficient Edge Deployment of Quantized YOLOv4-Tiny for Aerial Emergency Object Detection on Raspberry Pi 5 Focal loss for dense object detection,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:55:17.387682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:55:17.206707Z digest=sha256:e5e05cd866d3203f6c88b7e7af0fdea8529ded18daadf6889991dd61ddf4b30b

Observation ee86aed3-995f-4827-9a48-e0a60d3df4d4 · outbound

This paper cites Benchmarking TinyML Systems: Challenges and Direction.

Efficient Edge Deployment of Quantized YOLOv4-Tiny for Aerial Emergency Object Detection on Raspberry Pi 5 Benchmarking TinyML Systems: Challenges and Direction

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-07T04:55:17.127342Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:55:17.127342Z digest=sha256:417e068dc15bbd3dce7b018b9bde1927d525873b9e0f381b2b4b8686d06eb5dc

Pith citing papers

No inbound Pith citation observations are available.