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Paper Citation Record · LEDGER

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery

As of 10 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2507.12727.

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

pith.paper-citation-record.v1
2507.12727 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:44:52.407489Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T16:00:56.890985Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T09:26:02.871000Z

Reference resolution

25 of 25 outbound references displayed

  • verified exact0
  • verified fuzzy20
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c8eb7386-ce85-4c53-8c11-f07674890a59 · outbound

This paper cites From unmanned systems to autonomous intelligent systems,.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery From unmanned systems to autonomous intelligent systems,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:55.934347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:50.334528Z digest=sha256:825b68dce5c575960103584ada64c2dfc2b7dfa13fd8f81287c816a77162782f

Observation 17f5dc10-c11b-4aae-a374-fdcd462bceee · outbound

This paper cites Rich feature hierarchies for accu- rate object detection and semantic segmentation,.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery Rich feature hierarchies for accu- rate object detection and semantic segmentation,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:55.811154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:50.396778Z digest=sha256:78fca5f40e26ecc401467f75892318bf5b46ef09ccf4610b6fe0a304549ebbf3

Observation 1206b3cf-1a81-49b8-a433-90898af080a5 · outbound

This paper cites Microsoft COCO: common objects in context,.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery Microsoft COCO: common objects in context,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:55.656346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:50.491376Z digest=sha256:7dd790cd4fe6b3ca9d4d3bf3055b98a3c3977309ddb351607dfaa58ff5f6756e

Observation 0df4453a-0a79-413d-9169-1107c87a7f61 · outbound

This paper cites SSD: single shot multibox detector,.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery SSD: single shot multibox detector,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:55.464302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:50.622465Z digest=sha256:31a82355581d360808c09ead382941702757f7b92cb09c14b96a2d5d4652304d

Observation 80acf1d0-f6c1-468e-9cb0-1014ae3db6b6 · outbound

This paper cites Faster R-CNN: towards real-time object detection with region proposal networks,.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery Faster R-CNN: towards real-time object detection with region proposal networks,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:55.313629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:50.713795Z digest=sha256:b376009eaccf8d42098eb877b8d3cb9e0f110bc343f5ed131f471d09b17a57d9

Observation 3330df07-e493-4d4c-9936-7ef029b4cf03 · outbound

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

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery You only look once: unified, real-time object detection,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:55.068267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:50.788365Z digest=sha256:f591709828f614259c919f7684d4857d80fdd3855cb55fcf995b94a3b7e77da2

Observation 65ef1bfb-7f19-42c0-a246-9439e3f007d1 · outbound

This paper cites YOLO9000: better, faster, stronger,.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery YOLO9000: better, faster, stronger,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:54.912105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:50.863325Z digest=sha256:e0c42cfc8fc5874bc6aeb85211aaa1bf149ea2554558c96f3f92044a7bcf0e7c

Observation 90e4f245-e76b-4421-837c-104438fed829 · outbound

This paper cites YOLOv3: An Incremental Improvement.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery YOLOv3: An Incremental Improvement

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T16:44:50.958705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:44:50.958705Z digest=sha256:10be06079c4d2c356925d3deef3eb5607b18bf9801248605b11490e25d7ff0b5

Observation 84248369-a522-45f7-bedc-86e966a0e23c · outbound

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

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T16:44:51.107538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:44:51.107538Z digest=sha256:33feff9666ae1bc14e533d3adf12bb204321baaf5e509634709e63c8b6957a94

Observation 7ff011dd-84b2-4bfa-865f-1e94e6a9fe06 · outbound

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

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery You only look once: Unified, real-time object detection,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:54.703587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:51.162062Z digest=sha256:dfc69e9db2ff1cac343955c3334b79da9c3d22502e49549ffdfac071f940d823

Observation 855715d5-598e-4c7e-bcbb-10f9d3d98a25 · outbound

This paper cites YOLOv7: Trainable bag-of- freebies sets new state-of-the-art for real-time object detectors,.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery YOLOv7: Trainable bag-of- freebies sets new state-of-the-art for real-time object detectors,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:54.544860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:51.236165Z digest=sha256:90c0fa222d8758416f2c872cbb49faa0fcd1081bc8b9b2fbb44d33adf214a140

Observation 40df2cea-2ae9-4643-ab3a-13122a5035e6 · outbound

This paper cites YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T16:44:51.317026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:44:51.317026Z digest=sha256:f5b984e7236196601c24f78292633b545a4985b6ef00f1fa721ac7c48de37f30

Observation d9f5517a-b8d1-48ab-94bc-03ea7e8b3124 · outbound

This paper cites YOLOv10: Real-Time End-to-End Object Detection.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery YOLOv10: Real-Time End-to-End Object Detection

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T16:44:51.384980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:44:51.384980Z digest=sha256:9dbb2868a1e54d99973fb382cc96fb0c0a995348ea02022f80a06e8ef4c7151b

Observation aa228afe-b481-4711-853d-fa55f3723b50 · outbound

This paper cites VisDrone-DET2019: The vision meets drone object detection in im- age challenge results,.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery VisDrone-DET2019: The vision meets drone object detection in im- age challenge results,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:54.321606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:51.456847Z digest=sha256:dc3645dd5bd25d0675df7edbd7f2f1bc0add2c71d8e0ea4090df8eeba747c188

Observation 8990748b-bd33-4b83-b066-8a052d022fc5 · outbound

This paper cites ASF-YOLO: A novel YOLO model with attentional scale sequence fusion for cell instance segmentation,.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery ASF-YOLO: A novel YOLO model with attentional scale sequence fusion for cell instance segmentation,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:54.174230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:51.528941Z digest=sha256:6893e7290e87ff5e33adc39f0d073dfe1fd5650c112c0a0df66c2e80f3ccee17

Observation f159ea26-b35e-4fa2-93c5-09645a1e1bbc · outbound

This paper cites CSPNet: A new backbone that can enhance learning capability of CNN,.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery CSPNet: A new backbone that can enhance learning capability of CNN,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:54.028309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:51.635603Z digest=sha256:c73e9e11305966f7b5d6c989ec79f04e8ba66c49e2b9683b2b6f6fe60783fc1c

Observation 5c35d71a-7ea3-43f3-aad0-a56582f46692 · outbound

This paper cites EfficientNet: Rethinking model scaling for convolutional neural networks,.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery EfficientNet: Rethinking model scaling for convolutional neural networks,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:53.818117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:51.711889Z digest=sha256:771b143d06ab0a1a0e0b9f91942a552f3568857e190dc817d675452a2d670c06

Observation 457ed109-34ce-476b-95bb-53be8f2cb1f1 · outbound

This paper cites Soft-NMS: improving object detection with one line of code,.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery Soft-NMS: improving object detection with one line of code,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:53.649204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:51.782066Z digest=sha256:f74bc27315a16c88e90f7cfc9b2aacac6e80adcd3cdfa36e9ca2851824746965

Observation 300f7a13-633e-42a7-b849-a98e441f044c · outbound

This paper cites Session Peering Provisioning Framework (SPPF),.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery Session Peering Provisioning Framework (SPPF),

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:53.485032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:51.861882Z digest=sha256:0ea87912a53663ef1065bdd0fdb0b0f94be742e84f598cad6a3b2fc2034f2826

Observation a3b4b574-8162-4f53-b469-9a317faa7565 · outbound

This paper cites EdgeYOLO: An edge-real-time object detector,.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery EdgeYOLO: An edge-real-time object detector,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:53.295699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:51.996049Z digest=sha256:ef6e1fdc966a227b59c72c0e2ad7f4f3cdd0a1d96a30f0a6b92ed05bc7df1309

Observation fa06b5b5-ffe9-4a7c-a2bb-1e91a4cf78ff · outbound

This paper cites SSD: Single Shot MultiBox Detector,.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery SSD: Single Shot MultiBox Detector,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:53.141337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:52.111424Z digest=sha256:933271b331f0e3daba732cbee9c5447d08b99aede2954a16ec328f86196d9d4b

Observation dcefebbf-7ac3-4279-bdf5-4223fdab5c4f · outbound

This paper cites Object Detection with Deep Learning: A Review.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery Object Detection with Deep Learning: A Review

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-06T16:44:52.189005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:44:52.189005Z digest=sha256:54fd9b2e0e34a83d1d7534cf74f4c9e0f952d9ad5e565dc7f9cfa3e315a29e26

Observation 5445b406-10af-4980-9689-cc27ea04c667 · outbound

This paper cites Perceptual Generative Adversar- ial Networks for Small Object Detection,.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery Perceptual Generative Adversar- ial Networks for Small Object Detection,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:52.990550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:52.269376Z digest=sha256:c9fff30c3f980a07bd62cba0b91be764a1040cc0ff06d2dc38859dd1dbd36eb2

Observation 562e0aa4-b2d6-4dd3-8df8-09083892ad7c · outbound

This paper cites The Unmanned Aerial Vehicle Bench- mark: Object Detection and Tracking,.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery The Unmanned Aerial Vehicle Bench- mark: Object Detection and Tracking,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:52.800925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:52.339564Z digest=sha256:2aa6b925c756b65653adbb40f909b010e4b7d12ed4e579304517107ccb63c056

Observation 74b4600c-6858-43a8-93e9-bffff595061f · outbound

This paper cites Efficient Non-Maximum Suppression,.

SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery Efficient Non-Maximum Suppression,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:44:52.632402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:44:52.407489Z digest=sha256:82fd476a84f39db252c25e2c28dd8dc4ace7289306ccb80edf2fb7fe922736c3

Pith citing papers

Observation f56546ac-1c0e-46de-b8ae-73ae05eedb0f · inbound

DroneScan-YOLO: Redundancy-Aware Lightweight Detection for Tiny Objects in UAV Imagery cites this paper.

DroneScan-YOLO: Redundancy-Aware Lightweight Detection for Tiny Objects in UAV Imagery SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:26:02.874073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T16:00:56.890985Z digest=sha256:7d785f5cf73695c85729ad52f9a7f65c59e47370deff00006c4593f3675e8584