Paper Citation Record · LEDGER

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches

As of 8 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2506.00154.

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

pith.paper-citation-record.v1
2506.00154 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:14:40.442532Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

29 of 29 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d841f0a0-ea28-4471-bc05-a2f8c8a49721 · outbound

This paper cites an unresolved cited work.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:14:46.833054Z

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Observation 675991f2-a4e1-4617-8ce5-43de9d37b590 · outbound

This paper cites Remote sensing image data and automated analysis to describe marine bird distributions and abundances,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Remote sensing image data and automated analysis to describe marine bird distributions and abundances,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:46.594087Z

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This paper cites Proyecto Pantano,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Proyecto Pantano,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:46.345778Z

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Observation a925bd3a-cf3a-48ad-8daa-3901c8dc4fea · outbound

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

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches You Only Look Once: Unified, Real-Time Object Detection ,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:46.212814Z

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Observation 43fa8e46-f12c-4a48-be56-5e111075d6af · outbound

This paper cites Ultralytics YOLO,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Ultralytics YOLO,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:46.027688Z

Source-reported events for the cited work

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Observation ae94a18b-41c9-498d-a5ed-24459f287d62 · outbound

This paper cites DETRs Beat YOLOs on Real-Time Object Detection,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches DETRs Beat YOLOs on Real-Time Object Detection,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:45.872150Z

Source-reported events for the cited work

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Observation ae9fb8c4-4adb-46d5-bef5-05b1c0a501a0 · outbound

This paper cites Are unmanned aircraft systems (uas s) the future of wildlife monitoring? a review of accomplishments and challenges,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Are unmanned aircraft systems (uas s) the future of wildlife monitoring? a review of accomplishments and challenges,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:45.760563Z

Source-reported events for the cited work

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Observation ba5daeea-2c75-4df8-8eae-043d05f6c482 · outbound

This paper cites Increasing the accuracy and efficiency of wildlife census with unmanned aerial vehicles: a simulation study,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Increasing the accuracy and efficiency of wildlife census with unmanned aerial vehicles: a simulation study,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:45.463132Z

Source-reported events for the cited work

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Observation 3e8eaee3-15d1-44a8-a4f5-1b93ebfc8cc5 · outbound

This paper cites Unmanned aerial vehicle surveys reveal unexpectedly high density of a threatened deer in a plantation forestry landscape,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Unmanned aerial vehicle surveys reveal unexpectedly high density of a threatened deer in a plantation forestry landscape,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:45.247859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6193bf2e-edba-46a3-8c1c-351fea59532b · outbound

This paper cites Computer-automated bird detection and counts in high-resolution aerial images: A review,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Computer-automated bird detection and counts in high-resolution aerial images: A review,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:44.992764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 382a5dc2-bcb2-4ff7-a693-fa669d5ee480 · outbound

This paper cites Use of unmanned aerial vehicles for livestock monitoring based on streaming k-means clustering,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Use of unmanned aerial vehicles for livestock monitoring based on streaming k-means clustering,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:44.677406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1190e0f4-8c13-46d4-820c-e2308325c6c3 · outbound

This paper cites Fsscaps-detcountnet: Fuzzy soft sets and capsnet-based detection and counting network for monitoring animals from aerial images,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Fsscaps-detcountnet: Fuzzy soft sets and capsnet-based detection and counting network for monitoring animals from aerial images,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:44.406726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 44c63db0-bb49-45dd-8459-8660608c84fa · outbound

This paper cites A study on the detection of cattle in uav images using deep learning,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches A study on the detection of cattle in uav images using deep learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:44.103719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d4191bad-62da-44fc-a88e-26f2a16e9d40 · outbound

This paper cites Automated aerial animal detection when spatial resolution conditions are varied,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Automated aerial animal detection when spatial resolution conditions are varied,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:43.816840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9b8c6517-1c9b-46fd-8f94-08c8d4a2cd21 · outbound

This paper cites A survey of transfer learning,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches A survey of transfer learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:43.454453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a40133f8-fdbd-44d8-8063-10ba273c4838 · outbound

This paper cites Self-supervised pretraining and controlled augmentation improve rare wildlife recognition in uav images,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Self-supervised pretraining and controlled augmentation improve rare wildlife recognition in uav images,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:43.178299Z

Source-reported events for the cited work

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Observation 67ded6c4-10f6-4ec0-869d-cd4514513b8c · outbound

This paper cites Operational data augmentation in classifying single aerial images of animals,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Operational data augmentation in classifying single aerial images of animals,

Reference 17

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Observation c51ca516-2034-487e-ae46-44cca40072b5 · outbound

This paper cites Best practices to train deep models on imbalanced datasets—a case study on animal detection in aerial imagery,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Best practices to train deep models on imbalanced datasets—a case study on animal detection in aerial imagery,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:42.630690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c60f853c-962e-46dc-8652-d45c6a563017 · outbound

This paper cites W AID: A Large-Scale Dataset for Wildlife Detection with Drones,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches W AID: A Large-Scale Dataset for Wildlife Detection with Drones,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:42.415097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 10fb4e47-6b05-49a0-9e47-998d659c53c5 · outbound

This paper cites Deer survey from drone thermal imagery using enhanced faster R-CNN based on ResNets and FPN,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Deer survey from drone thermal imagery using enhanced faster R-CNN based on ResNets and FPN,

Reference 20

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Source-reported events for the cited work

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Observation 98dd8a13-8525-4384-8ec4-88c172f61525 · outbound

This paper cites Efficient endangered deer species monitoring with uav aerial imagery and deep learning,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Efficient endangered deer species monitoring with uav aerial imagery and deep learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:41.863374Z

Source-reported events for the cited work

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Observation 3f63a186-8e79-459d-93c9-431150e2b81e · outbound

This paper cites An end-to-end transformer model for 3d object detection,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches An end-to-end transformer model for 3d object detection,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:41.634844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation aa6944ae-da67-4a05-bd57-4f3a51ada332 · outbound

This paper cites Deep residual learning for image recognition,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Deep residual learning for image recognition,

Reference 23

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

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Microsoft COCO: Common Objects in Context,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:41.487674Z

Source-reported events for the cited work

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Observation 90b2260c-8c44-4978-b75a-4e2b90e5ffca · outbound

This paper cites Mask R-CNN,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Mask R-CNN,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:41.276551Z

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Observation 16ab4035-adff-4c2e-86fa-09cc51f07b3b · outbound

This paper cites Spatial pyramid pooling in deep convolutional networks for visual recognition,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches Spatial pyramid pooling in deep convolutional networks for visual recognition,

Reference 26

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

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches CSPNet: A new backbone that can enhance learning capability of CNN,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:41.031517Z

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Observation f15dc336-1631-40e0-872e-a4efe388aae8 · outbound

This paper cites CBAM: Convolutional block attention module,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches CBAM: Convolutional block attention module,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:40.883989Z

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Observation 0eab64d7-3132-4210-827e-beb98cf0f3ef · outbound

This paper cites YOLACT: Real-time instance segmentation,.

Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study Between Efficient Deep Learning Approaches YOLACT: Real-time instance segmentation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:14:40.725515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.