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Paper Citation Record · LEDGER

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data

As of 15 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2411.09077.

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

pith.paper-citation-record.v1
2411.09077 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:09:59.341636Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

57 of 57 outbound references displayed

  • verified exact3
  • verified fuzzy49
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9f6d31c0-fb9b-4f24-bd04-95e9672bd422 · outbound

This paper cites Increase in use of drones for prison smuggling,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Increase in use of drones for prison smuggling,

Reference 1

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation fb690aff-03fe-47f6-b68e-1228b588383f · outbound

This paper cites Drugs, weapons ’smuggled to prisoners by drone’,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Drugs, weapons ’smuggled to prisoners by drone’,

Reference 2

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation d5755641-ac8a-4a73-b975-82e18dd21f59 · outbound

This paper cites Heathrow airport: Drone sighting halts departures,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Heathrow airport: Drone sighting halts departures,

Reference 3

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Observation c2c19f6c-07f0-48fb-827d-72feb0abeb70 · outbound

This paper cites Flights diverted at East Midlands airport after drone sightings,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Flights diverted at East Midlands airport after drone sightings,

Reference 4

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.111018Z digest=sha256:c332413b15db2f5a4d7c4ceff3289ec8c101a7103c35d14c63e37589073e7429

Observation e4c106db-1cfa-42e3-a954-319e422ebf00 · outbound

This paper cites Dublin airport: Flights suspended for 30 minutes after drone sightings,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Dublin airport: Flights suspended for 30 minutes after drone sightings,

Reference 5

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source=pdf_text observed=2026-08-12T21:09:59.114944Z digest=sha256:35a21d50a223906abff4534872631134457251fb967e51185c46ad68567e60b1

Observation cdbce941-2b05-406c-be08-b036e7bfc31e · outbound

This paper cites UK Counter-Unmanned Aircraft Strategy,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data UK Counter-Unmanned Aircraft Strategy,

Reference 6

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation fc0b7e53-39a8-44c5-84b9-947075db4cea · outbound

This paper cites Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Tech- nologies,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Tech- nologies,

Reference 7

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation ae3f6457-9e4a-48b3-9c6c-7305a5d6d52f · outbound

This paper cites ImageNet classification with deep convolutional neural networks,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data ImageNet classification with deep convolutional neural networks,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.127721Z digest=sha256:93f1788e997cc08f11565a45b96f9fe15284efa15bb206e0b0227af930ff56ba

Observation fef51cd3-a40c-4aa1-818a-eed10206cde1 · outbound

This paper cites You Only Look Once: Unified, Real-Time Object Detection,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data You Only Look Once: Unified, Real-Time Object Detection,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.131469Z digest=sha256:a4f28313ebabeff9653dcaf859b0d6248cf838d831511966d2d459f2da2b6ca3

Observation 98944eba-ba2f-465e-a094-471ddabcd589 · outbound

This paper cites YOLO9000: Better, Faster, Stronger,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data YOLO9000: Better, Faster, Stronger,

Reference 10

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 7ffc33e0-1703-41d9-af46-e94c3e1b47c8 · outbound

This paper cites SSD: Single Shot MultiBox Detector,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data SSD: Single Shot MultiBox Detector,

Reference 11

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.139155Z digest=sha256:e39df04f2e43c100f9c5a805c840dcb2e80b6b2c5a59d1ebf0217a550ee2e166

Observation ee4f2d5b-60e6-49d3-8fd1-49c36ac711f0 · outbound

This paper cites Fast R-CNN,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Fast R-CNN,

Reference 12

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.143105Z digest=sha256:f7eebbb6675f9ac8cc2f6a67af54a1cd9a1add88b7f69184d771633df1cdf7c9

Observation cef68868-403b-428f-a56c-ae4933a7b1f0 · outbound

This paper cites Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:09:59.147860Z digest=sha256:47f3639d471089e2f9d317ed72f350708100a02e7018b8e7ebc05de72f54d007

Observation 8b111258-e9d7-4aaa-bc8a-ffcc795d90b1 · outbound

This paper cites Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark,

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.152153Z digest=sha256:d20c1e555a42d8db74a8b1cef4dc0ba0c5789e371db125e8ae1dc336fa2c8b0f

Observation 3adc6508-6eda-4702-ae00-2494534ab5e6 · outbound

This paper cites Attention Is All You Need,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Attention Is All You Need,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.156314Z digest=sha256:cc71dda2104352255bccff0662e7027de1a4aed834a364df5f96de95833710f2

Observation 837aa9ae-40a2-47b5-bbca-7566d4ae8846 · outbound

This paper cites End-to-End Object Detection with Transformers,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data End-to-End Object Detection with Transformers,

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.160554Z digest=sha256:9a4f5a6a3cf9bcc4a0dbd948d6ead933c7584f6789ca8bba9f01a362d56a7396

Observation 1fdeb694-56aa-4d72-9c6c-d2485237620d · outbound

This paper cites Towards Out-Of-Distribution Generalization: A Survey,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Towards Out-Of-Distribution Generalization: A Survey,

Reference 17

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.164702Z digest=sha256:874bb057642ec906f40816f795f8fcc473d9264100bfc598b9668eedfe999031

Observation 82262cd6-00eb-4a7f-ac02-f82516695605 · outbound

This paper cites A Comprehensive Survey on Transfer Learning,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data A Comprehensive Survey on Transfer Learning,

Reference 18

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.169264Z digest=sha256:17e7109cae79a246aebaa6e7b11c42eb963a602d18ee94100602fdc6b2596cd8

Observation 9821e417-923b-4bf2-a2e9-9fede08e7a88 · outbound

This paper cites Domain Adaptation for Visual Applications: A Comprehen- sive Survey,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Domain Adaptation for Visual Applications: A Comprehen- sive Survey,

Reference 19

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.173382Z digest=sha256:404db88ee3a35b33c014ce2feab03189623aaa775e8a49a5c82367c1b6a9ac96

Observation 225511dc-15cc-46b6-b82f-5a254fb987b8 · outbound

This paper cites Domain General- ization: A Survey,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Domain General- ization: A Survey,

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.178284Z digest=sha256:cf74daf2d8f6f3d36c82aa1354c9f54860e27152e46f423fe9d985be0b9b4915

Observation 7bfe6b0d-073f-4862-a771-374950fbfae8 · outbound

This paper cites Domain randomization for transferring deep neural networks from sim- ulation to the real world,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Domain randomization for transferring deep neural networks from sim- ulation to the real world,

Reference 21

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.182259Z digest=sha256:326d81330da53bc149937040f31c2fa4515a3fd0e1d8900d758bf5b923cfcce9

Observation 20bc96ab-7d98-4212-82e3-0202ed064c19 · outbound

This paper cites Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:09:59.185848Z digest=sha256:4f9943ac04cce870cd04dd54897adf7a6183b42c8d5d1cc6039c09ef0f424e5d

Observation 0241026b-2b04-429f-bb4e-2ec5876930e0 · outbound

This paper cites Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data,

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.191721Z digest=sha256:6516ff9befc61a36c1d798aac6ecfe0c4aa864d567c99137b2ed5be7cfb5bc8a

Observation f95e9be3-5e9a-4296-9db6-12523c2f6a32 · outbound

This paper cites Applying Domain Randomization to Synthetic Data for Object Category Detection,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Applying Domain Randomization to Synthetic Data for Object Category Detection,

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.196874Z digest=sha256:3bd38fd3a9166ef7e07bd8dcfc2e3479d7f09466afff7f6d1970954001723a63

Observation c73690a6-c206-41b3-919e-de8fc6c5c732 · outbound

This paper cites Benchmarking Domain Randomisation for Visual Sim-to-Real Transfer,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Benchmarking Domain Randomisation for Visual Sim-to-Real Transfer,

Reference 25

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raw_fallback, observed 2026-08-12T21:09:59.857863Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.201144Z digest=sha256:3e031f22f86d119e0e5294b56ed0feb3d0858228458bcd2ac42cda915f9ea9e1

Observation 8781fbb6-5531-443e-b418-32310833ecba · outbound

This paper cites On Pre-Trained Image Features and Synthetic Images for Deep Learning.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data On Pre-Trained Image Features and Synthetic Images for Deep Learning

Reference 26

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local_arxiv, observed 2026-08-12T21:09:59.437939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.204838Z digest=sha256:053d1e53837593e15cd5bed002655a43056908d5f24fe78fefa975f526de127b

Observation 63829c2e-c99b-43d7-b4a8-f57b4f16a854 · outbound

This paper cites Drone Detection Using YOLOv5,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Drone Detection Using YOLOv5,

Reference 27

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raw_fallback, observed 2026-08-12T21:09:59.842268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.209009Z digest=sha256:9b1b24949ba1877a8bf726140486a07dc94bb328e58b9df00cbab5e26084fdf9

Observation 487f1f2a-fe23-4c95-ae80-f1fd1037ad07 · outbound

This paper cites Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery,

Reference 28

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raw_fallback, observed 2026-08-12T21:09:59.827899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.213936Z digest=sha256:8e2cb22555004364f654c485fade35f10b7a9c13f6764947691481a84d0f8bb8

Observation 0bc7e76a-7f7e-466f-8d05-36c28a995e2b · outbound

This paper cites An Object Detection Algorithm for Rotary- Wing UA V Based on AWin Transformer,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data An Object Detection Algorithm for Rotary- Wing UA V Based on AWin Transformer,

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.218035Z digest=sha256:052532153dbfdc17e8e42867d97cacd099674dddb4e9d6ff91ba31560a03674b

Observation ce02ff3c-78c3-4bb0-b886-56d47af9f402 · outbound

This paper cites A Modified YOLOv4 Deep Learning Network for Vision-Based UA V Recognition,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data A Modified YOLOv4 Deep Learning Network for Vision-Based UA V Recognition,

Reference 30

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raw_fallback, observed 2026-08-12T21:09:59.801926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.223708Z digest=sha256:9cc6659afeab805d75fd5361f46f1e2a5f3a5a2e3876bf5dda0ea037a845523f

Observation b6e5da38-9167-46ca-8a16-24194bc70d4b · outbound

This paper cites Exploitation of data augmentation strategies for improved UA V detection,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Exploitation of data augmentation strategies for improved UA V detection,

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.227596Z digest=sha256:16ac1cad3a4f7b83cef7f71286c953ef17f5f0c90ee64651b0e8a494ecb6853e

Observation 09519139-071b-42bf-841a-617392d9844c · outbound

This paper cites Detecting aerial objects: Drones, birds, and helicopters,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Detecting aerial objects: Drones, birds, and helicopters,

Reference 32

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raw_fallback, observed 2026-08-12T21:09:59.777951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.231508Z digest=sha256:d46d9562c7d2208a2da6aee36eb845b558f0d3a7c29f054d25c443ac5804cccd

Observation 6d6a099a-428f-40e6-bd8e-5205428b3f7a · outbound

This paper cites Small Flying Object Detection and Tracking in Digital Airport Tower through Spatial- Temporal ConvNets,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Small Flying Object Detection and Tracking in Digital Airport Tower through Spatial- Temporal ConvNets,

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.235358Z digest=sha256:3df6b720bae0a79b43299d08b62963276bae21828911b715c898953eac3b1b54

Observation 5f0bfb5b-8f14-4052-bbea-a62b1f7b7791 · outbound

This paper cites Spatio-Temporal Semantic Segmentation for Drone Detection,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Spatio-Temporal Semantic Segmentation for Drone Detection,

Reference 34

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raw_fallback, observed 2026-08-12T21:09:59.747050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.239621Z digest=sha256:f911dc933c6ce311b44c4f27cbcc48dbad342a5a3deb10f089bc4a0264b48c0f

Observation 795c7b50-34eb-4586-b39e-5b3db79dedf9 · outbound

This paper cites TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detec- tion in Aerial Videos,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detec- tion in Aerial Videos,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:09:59.732057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.244390Z digest=sha256:3b184d1821f2c21be19f2a2a91878f7f5256a03f64eea40258d7e4cf6e828144

Observation e3241ff1-86fe-4079-89a0-51bdbbb6cf66 · outbound

This paper cites On Rendering Synthetic Images for Training an Object Detector,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data On Rendering Synthetic Images for Training an Object Detector,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:09:59.719706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.248885Z digest=sha256:2f420116b7e0f3685d1007486ce1d19b14d4c653f2c8d27735cf4b2c0a5c818d

Observation e4e017e9-306a-4883-a8ff-76a47131c054 · outbound

This paper cites UA V detection with a dataset augmented by domain randomization,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data UA V detection with a dataset augmented by domain randomization,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:09:59.706781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.253061Z digest=sha256:02afe11f3ad9861c5128c242a3a01bf47f5fcb7fad4a2dca93f6e7f54469eb00

Observation d4007aec-43c4-4f45-a9d3-f574729840e7 · outbound

This paper cites Using Images Rendered by PBRT to Train Faster R-CNN for UA V Detection,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Using Images Rendered by PBRT to Train Faster R-CNN for UA V Detection,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:09:59.692600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.256881Z digest=sha256:6fe0e31447a54bca7e86764464ae672c00d911fe2b6869a13be53d2d4859d7d5

Observation b04678f8-d9d3-438a-b23d-4eb8eabc05d1 · outbound

This paper cites an unresolved cited work.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Unresolved cited work

Reference 39

Resolution
unresolved
raw_fallback, observed 2026-08-12T21:09:59.680427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.261581Z digest=sha256:e7db45fe6bd5ad75d9b8e9db81f7c96ac530d1548b2580a9cc98448f8cd5cf05

Observation c6cdc159-ed38-4648-b507-48dbc62be12c · outbound

This paper cites Quantifying the Simulation–Reality Gap for Deep Learning-Based Drone Detection,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Quantifying the Simulation–Reality Gap for Deep Learning-Based Drone Detection,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:09:59.666652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.265996Z digest=sha256:a564b2010e4e2ee0f6f1c35381288094805e323cec9eeda2603e8aef8f28a2c1

Observation 2b61df76-16dd-4864-96da-3473a86aa29d · outbound

This paper cites DronePose: The identification, segmentation, and orientation detection of drones via neural networks.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data DronePose: The identification, segmentation, and orientation detection of drones via neural networks

Reference 41

Resolution
verified exact
local_arxiv, observed 2026-08-12T21:09:59.418816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.271343Z digest=sha256:7ade088aabe1fcb3d38c4a582e812f92e026217b96477c5eb80adf4ede6ca9d3

Observation 2315893c-1d87-46da-8953-a4ad362cdaf3 · outbound

This paper cites Scarce Data Driven Deep Learning of Drones via Generalized Data Distribution Space.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Scarce Data Driven Deep Learning of Drones via Generalized Data Distribution Space

Reference 42

Resolution
verified exact
local_arxiv, observed 2026-08-12T21:09:59.395949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.276142Z digest=sha256:12d98f6ae1277ff2f5899de0bfd60562037fdee731a15a60bb12128c994532c7

Observation 5db4a0a3-bdea-4c5f-a86a-b82d6fe68109 · outbound

This paper cites Drone Detection Using Depth Maps,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Drone Detection Using Depth Maps,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:09:59.653295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.280354Z digest=sha256:b0a89951e88f7d03d25a450f66d1a50fdb9c54df715b10b461f659f8304df56a

Observation 769fd070-6b0e-4b40-9eeb-49b15d8e5e11 · outbound

This paper cites Drone Model Classification Using Convolutional Neural Network Trained on Synthetic Data,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Drone Model Classification Using Convolutional Neural Network Trained on Synthetic Data,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:09:59.640074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.284906Z digest=sha256:b1c20c59752c15a955637d1787af33c8149111966ecaf2851488987e247cfa68

Observation cfb69e25-9766-4df6-9ed4-cfd65aeae792 · outbound

This paper cites Drone Model Identification by Convolutional Neural Network from Video Stream,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Drone Model Identification by Convolutional Neural Network from Video Stream,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:09:59.621450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.291055Z digest=sha256:e284dadc60b36d4a3760620f6151882bdc717ddfd27009e2035dfe701e6804e9

Observation a87b68ae-9a9e-485b-9d28-ae6e81c0f4d0 · outbound

This paper cites Blender - a 3D modelling and rendering package.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Blender - a 3D modelling and rendering package

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:09:59.609244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.295447Z digest=sha256:2140bc22f3e351c0e8daad029c997a4be2c4486de5374cc9359d8f0eafec5b71

Observation e5db959b-7f43-4df3-a605-f22fa549f4b7 · outbound

This paper cites Microsoft COCO: Common Objects in Context,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Microsoft COCO: Common Objects in Context,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:09:59.596724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.300504Z digest=sha256:d95cce59344f79ae6fbc80edaa38198f4cc33b1fc41e548c4ff7362d60519968

Observation 46d2fb38-c929-48d9-9f5d-a1d4eab5d082 · outbound

This paper cites PyTorch: An Imperative Style, High- Performance Deep Learning Library,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data PyTorch: An Imperative Style, High- Performance Deep Learning Library,

Reference 48

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verified fuzzy
raw_fallback, observed 2026-08-12T21:09:59.583023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.304781Z digest=sha256:17dbe71fc96e2ff015349aa593f15f12ff9ae202c666722efdcc97710f1ea4d4

Observation 0f218152-8070-4de5-a0f3-1d14299aa585 · outbound

This paper cites Vision/torchvision/models/detection/faster rcnn.py at main · pytorch/vision,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Vision/torchvision/models/detection/faster rcnn.py at main · pytorch/vision,

Reference 49

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verified fuzzy
raw_fallback, observed 2026-08-12T21:09:59.570494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.309251Z digest=sha256:4e574e987c1fd428af76ce64d19cf7296b9c54b118bad8b92832eb680c366c1b

Observation 0e3c7204-5d9b-4c02-9096-d434044436b8 · outbound

This paper cites On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:09:59.556793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.313264Z digest=sha256:94a3ea2068e0d5b820e2708e7af99700ae4f9cf069491e9484b1cdaab8101c9c

Observation 999e9be8-3960-43a2-8a18-72a25a51690f · outbound

This paper cites Jung, “Imgaug,” Nov.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Jung, “Imgaug,” Nov

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:09:59.543560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.316822Z digest=sha256:2f8b13643b08caacd7737fef5a5f2230bcf786511ea54397306f7513ac7fafbb

Observation 39e8f535-e428-4d53-afba-9c2ffba24d35 · outbound

This paper cites Adaptive Inattentional Framework for Video Object Detection With Reward-Conditional Training,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Adaptive Inattentional Framework for Video Object Detection With Reward-Conditional Training,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:09:59.531490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.320889Z digest=sha256:fa41516daf62109edde359c446af378e1e7807d36bff4a656b63e380f62abbbf

Observation c684c384-4fed-4a96-8260-51efaa7c78ad · outbound

This paper cites Drone-vs-Bird Detection Challenge at IEEE A VSS2019,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Drone-vs-Bird Detection Challenge at IEEE A VSS2019,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:09:59.519100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.324545Z digest=sha256:8c3a282c63eabefedb12c08d7eb8770e98cf934b3ba14e18cc8fe12b635446ac

Observation d15e10c9-2211-4e00-b027-875b035c8be9 · outbound

This paper cites Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:09:59.505209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.328635Z digest=sha256:2af30e766d92d88ed0eed981d4d67a552821a0fbef046a91b5fcc35708a17bcd

Observation 3571e08a-706c-4fe0-a872-d144f6e0c56b · outbound

This paper cites Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-12T21:09:59.332596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:09:59.332596Z digest=sha256:a057dd5727d6a8e4a6af009d69627f135016a96e0512c6e187e4d9d9d81ee840

Observation 26b530d4-0750-4a0f-853e-d633daae9297 · outbound

This paper cites Torch.manual seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision,.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Torch.manual seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T21:09:59.491067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.337245Z digest=sha256:ee20ebe9fee2063f7e3565adbf0d4330a4fd98cb6266b517c0cb06b4ab920bb2

Observation 4e2b3d2d-1dc7-466b-a5cd-df8604d350aa · outbound

This paper cites an unresolved cited work.

Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data Unresolved cited work

Reference 57

Resolution
unresolved
raw_fallback, observed 2026-08-12T21:09:59.476891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T21:09:59.341636Z digest=sha256:edefd59ef23c5b4285e12ed111f92b6c243e1482f85b366d516c3fed1e0d4903

Pith citing papers

No inbound Pith citation observations are available.