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Paper Citation Record · LEDGER

Tracking Moose using Aerial Object Detection

As of 22 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 0 inbound Pith citation observations for arXiv:2507.21256.

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

pith.paper-citation-record.v1
2507.21256 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T13:07:27.464202Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

62 of 62 outbound references displayed

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  • verified fuzzy10
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eef7b88f-5fdf-403c-b754-09e630b32124 · outbound

This paper cites ‘It’s like a connection between all of us’: Inuit social connectio ns and caribou declines in Labrador, Canada,.

Tracking Moose using Aerial Object Detection ‘It’s like a connection between all of us’: Inuit social connectio ns and caribou declines in Labrador, Canada,

Reference 1

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Observation 901b28d3-a33d-4797-a125-2b83db31aa19 · outbound

This paper cites Vulnerability of Inuit food systems to food i nsecurity as a consequence of cli- mate change: a case study from Igloolik, Nunavut,.

Tracking Moose using Aerial Object Detection Vulnerability of Inuit food systems to food i nsecurity as a consequence of cli- mate change: a case study from Igloolik, Nunavut,

Reference 2

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Observation 7e897b28-487b-43ea-ba2f-e41ceab832cf · outbound

This paper cites Response of moose to forest ha rvest and management: a literature review,.

Tracking Moose using Aerial Object Detection Response of moose to forest ha rvest and management: a literature review,

Reference 3

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Observation 469405f6-cb3e-4d93-87ee-2c54470cb142 · outbound

This paper cites A review of methods to estimate and monitor moose density and abundance,.

Tracking Moose using Aerial Object Detection A review of methods to estimate and monitor moose density and abundance,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 9ab9653e-cc28-4c40-9d6c-351d80209cda · outbound

This paper cites A comparison of unmanned aerial vehicles (drones) and manned helicopters for monitoring macropod populations,.

Tracking Moose using Aerial Object Detection A comparison of unmanned aerial vehicles (drones) and manned helicopters for monitoring macropod populations,

Reference 5

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Observation 5ba04c30-d3c3-4fb5-8dab-59dcfc4c435f · outbound

This paper cites Small-object detection for uav-based images using a distance metric method,.

Tracking Moose using Aerial Object Detection Small-object detection for uav-based images using a distance metric method,

Reference 6

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Observation 5db132b1-4e53-4278-a587-892564c8076b · outbound

This paper cites Deep learning wor kflow to support in-flight processing of digital aerial imagery for wildlife population surveys,.

Tracking Moose using Aerial Object Detection Deep learning wor kflow to support in-flight processing of digital aerial imagery for wildlife population surveys,

Reference 7

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Observation 60c1e4e0-b5b9-41db-8bfa-5a22157fa4e9 · outbound

This paper cites A survey of small object detection bas ed on deep learning in aerial images,.

Tracking Moose using Aerial Object Detection A survey of small object detection bas ed on deep learning in aerial images,

Reference 8

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Observation 389e2deb-e102-48be-a8ac-620cecafd2ae · outbound

This paper cites Small object bird detection in infrared drone videos using mask r- cnn deep learning,.

Tracking Moose using Aerial Object Detection Small object bird detection in infrared drone videos using mask r- cnn deep learning,

Reference 9

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Observation 768a375e-6c0f-46ff-b5c4-e067a9aa79a0 · outbound

This paper cites Prescribed grass fire mapping and rate of spread measurement using nir images from a small fixed-wing uas,.

Tracking Moose using Aerial Object Detection Prescribed grass fire mapping and rate of spread measurement using nir images from a small fixed-wing uas,

Reference 10

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Observation c9f37620-52f9-4099-a518-67cbe2c8ead6 · outbound

This paper cites Microsoft COCO: Common Objects in Context.

Tracking Moose using Aerial Object Detection Microsoft COCO: Common Objects in Context

Reference 11

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Observation 4d20381c-e459-49cd-9376-0d860de37276 · outbound

This paper cites The Pascal Visual Object Classes (VOC) Challenge,.

Tracking Moose using Aerial Object Detection The Pascal Visual Object Classes (VOC) Challenge,

Reference 12

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Observation f5f27b72-2655-47ac-8768-12ae92d9d325 · outbound

This paper cites Visual tra cking of small animals in cluttered natural environments using a freely moving camera,.

Tracking Moose using Aerial Object Detection Visual tra cking of small animals in cluttered natural environments using a freely moving camera,

Reference 13

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Observation cbbd6386-d4a8-44e9-af2f-d3953058eaac · outbound

This paper cites Wildlife monitoring with drones: A survey of end users,.

Tracking Moose using Aerial Object Detection Wildlife monitoring with drones: A survey of end users,

Reference 14

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation efb94588-4fb0-4f7d-b1c9-8e542e43c412 · outbound

This paper cites Au tomated detection of wildlife using drones: Synthesis, opportunities and constraints,.

Tracking Moose using Aerial Object Detection Au tomated detection of wildlife using drones: Synthesis, opportunities and constraints,

Reference 16

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Observation 0432d337-cf8c-449b-9284-989040458fe3 · outbound

This paper cites Ultralytics YOLO,.

Tracking Moose using Aerial Object Detection Ultralytics YOLO,

Reference 17

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 9586bd6d-3ec8-4063-83b7-b500f9bb81c8 · outbound

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

Tracking Moose using Aerial Object Detection Faster R-CNN: Tow ards Real-Time Object Detection with Region Proposal Networks,

Reference 18

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Observation 1e6c021b-6307-4336-b2b8-eacb3450c80f · outbound

This paper cites DETRs with Collaborative H ybrid Assignments Training,.

Tracking Moose using Aerial Object Detection DETRs with Collaborative H ybrid Assignments Training,

Reference 19

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 9c144938-516f-49c2-a0b1-ff41dda21515 · outbound

This paper cites A new dataset and comparative study for aphid cluster dete ction and segmentation in sorghum fields,.

Tracking Moose using Aerial Object Detection A new dataset and comparative study for aphid cluster dete ction and segmentation in sorghum fields,

Reference 20

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Observation e1ac4434-d73c-4fa1-a1fa-0c76293462b3 · outbound

This paper cites IEEE, 2023.

Tracking Moose using Aerial Object Detection IEEE, 2023

Reference 21

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Observation d20b71de-a988-4bd6-b317-a64e0c87f64b · outbound

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

Tracking Moose using Aerial Object Detection You Only Look Once: Unified, Real-Time Object Detection

Reference 22

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Observation fd7e8e0e-6597-41d1-99c5-2051d4a426e5 · outbound

This paper cites Improvements in Aerial Object Detection: C omparing YOLOv7 with YOLOv5 for Fine Drone and Bird Detection in V olatile Environments,.

Tracking Moose using Aerial Object Detection Improvements in Aerial Object Detection: C omparing YOLOv7 with YOLOv5 for Fine Drone and Bird Detection in V olatile Environments,

Reference 23

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Observation bcee3967-363c-4c7c-8e21-aecd8072adef · outbound

This paper cites R ecent Advances for Aerial Object Detection: A Survey,.

Tracking Moose using Aerial Object Detection R ecent Advances for Aerial Object Detection: A Survey,

Reference 24

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Observation 39a042b8-df42-49d7-9546-c8aa61879399 · outbound

This paper cites Research Towards Y olo-Series Algorithms: Co mparison and Analysis of Object Detection Models for Real-Time UA V Applications,.

Tracking Moose using Aerial Object Detection Research Towards Y olo-Series Algorithms: Co mparison and Analysis of Object Detection Models for Real-Time UA V Applications,

Reference 25

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Observation 0b4d0810-1e43-4172-bf09-b1ca2f7eb95c · outbound

This paper cites Comparison of YOL O V ersions for Object Detection from Aerial Images,.

Tracking Moose using Aerial Object Detection Comparison of YOL O V ersions for Object Detection from Aerial Images,

Reference 26

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation c92ba4e8-b42a-43bc-8189-bec953808fee · outbound

This paper cites Efficient-L ightweight YOLO: Improving Small Object Detection in YOLO for Aerial Images,.

Tracking Moose using Aerial Object Detection Efficient-L ightweight YOLO: Improving Small Object Detection in YOLO for Aerial Images,

Reference 27

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Observation 2b7f3417-f2d2-41f5-b0a1-6eb2531e901b · outbound

This paper cites Efficient Golf Ball Detection and Tracking Based on Convolutional Neural Networks and Kalman Filter.

Tracking Moose using Aerial Object Detection Efficient Golf Ball Detection and Tracking Based on Convolutional Neural Networks and Kalman Filter

Reference 28

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Observation 599a035c-f8b8-4340-8c51-86878a336654 · outbound

This paper cites YOLO-U A V: Object Detection Method of Unmanned Aerial V ehicle Imagery Based on Efficient Multi- Scale Feature Fusion,.

Tracking Moose using Aerial Object Detection YOLO-U A V: Object Detection Method of Unmanned Aerial V ehicle Imagery Based on Efficient Multi- Scale Feature Fusion,

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation f8bbe250-0107-442c-818c-a598a6d8a9b2 · outbound

This paper cites MBSDet: A Novel Method for Marin e Object Detection in Aerial Imagery with Complex Background Suppression,.

Tracking Moose using Aerial Object Detection MBSDet: A Novel Method for Marin e Object Detection in Aerial Imagery with Complex Background Suppression,

Reference 30

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 1f225607-62f5-4574-9cc7-f9a91cc2ef2c · outbound

This paper cites Oriented Bounding Box Representation Based on Continuous Encoding in Oriented SAR Ship Detection,.

Tracking Moose using Aerial Object Detection Oriented Bounding Box Representation Based on Continuous Encoding in Oriented SAR Ship Detection,

Reference 31

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Observation 4b02d4ff-ca12-456f-b4e0-bf920e7f1ff6 · outbound

This paper cites Aerial Imaging-Based Soiling Detection System for Solar Photovoltaic Panel Cleanliness Inspectio n,.

Tracking Moose using Aerial Object Detection Aerial Imaging-Based Soiling Detection System for Solar Photovoltaic Panel Cleanliness Inspectio n,

Reference 32

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Observation 2f018abb-4188-449d-971f-34ca82340409 · outbound

This paper cites CPDD: A Cross-Scenario Pho tovoltaic Defect Detector Based on Fine-Grained Feature Autoencoding and Pseudo-Box Contr astive Learning,.

Tracking Moose using Aerial Object Detection CPDD: A Cross-Scenario Pho tovoltaic Defect Detector Based on Fine-Grained Feature Autoencoding and Pseudo-Box Contr astive Learning,

Reference 33

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Observation 0055f2d2-cd2a-4896-ab24-efe2f92085ce · outbound

This paper cites Beyond sRGB: Optimizing O bject Detection with Diverse Color Spaces for Precise Wildfire Risk Assessment,.

Tracking Moose using Aerial Object Detection Beyond sRGB: Optimizing O bject Detection with Diverse Color Spaces for Precise Wildfire Risk Assessment,

Reference 34

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation d6335608-545f-45c2-9a85-e2a16b445617 · outbound

This paper cites Real-Time Aerial Multispectral O bject Detection with Dynamic Modality-Balanced Pixel-Level Fusion,.

Tracking Moose using Aerial Object Detection Real-Time Aerial Multispectral O bject Detection with Dynamic Modality-Balanced Pixel-Level Fusion,

Reference 35

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation d9094696-ce40-4985-b08d-75020e54eb14 · outbound

This paper cites Mask R-C NN,.

Tracking Moose using Aerial Object Detection Mask R-C NN,

Reference 36

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Observation d43be7ac-7c10-4a25-9a44-dfa387c9488d · outbound

This paper cites IEEE, 2019.

Tracking Moose using Aerial Object Detection IEEE, 2019

Reference 37

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Observation f47dc470-3b3d-4d3e-8eee-ef7d90a240e9 · outbound

This paper cites Focal Loss for Dense Object Detection.

Tracking Moose using Aerial Object Detection Focal Loss for Dense Object Detection

Reference 38

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Observation f6873eb5-c709-473d-a886-168ec4070c09 · outbound

This paper cites Multispecies detection and identification of African mammals in aerial imagery using convolutional ne ural networks,.

Tracking Moose using Aerial Object Detection Multispecies detection and identification of African mammals in aerial imagery using convolutional ne ural networks,

Reference 39

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doi, observed 2026-08-06T13:07:27.544337Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T13:07:27.055492Z digest=sha256:2b8f070ed553e0ea240408ca45883a96980ce763da9eb4ae954755bb7fcda903

Observation 01653af9-ba4c-4e52-8b8a-0cacaa49b655 · outbound

This paper cites Optimized faster R-CNN for oil wells detection from high-resolution remote s ensing images,.

Tracking Moose using Aerial Object Detection Optimized faster R-CNN for oil wells detection from high-resolution remote s ensing images,

Reference 40

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation b4c51382-659e-4821-8782-d4a5566720b0 · outbound

This paper cites An I mproved Deep Learning Approach for Retrieving Outfalls Into Rivers From UAS Image ry,.

Tracking Moose using Aerial Object Detection An I mproved Deep Learning Approach for Retrieving Outfalls Into Rivers From UAS Image ry,

Reference 41

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Observation 3c85c6f1-dfca-4dc7-9e3c-13876806db8e · outbound

This paper cites Analysis of the performance of Faster R-CNN and YOLOv8 in de tecting fishing vessels and fishes in real time,.

Tracking Moose using Aerial Object Detection Analysis of the performance of Faster R-CNN and YOLOv8 in de tecting fishing vessels and fishes in real time,

Reference 42

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doi, observed 2026-08-06T13:07:27.532649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 45edf5c4-b6ee-4eab-85e0-bf8d023519a5 · outbound

This paper cites Accumulated trivial attention matters in vision transformers on small datasets,.

Tracking Moose using Aerial Object Detection Accumulated trivial attention matters in vision transformers on small datasets,

Reference 43

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source=pdf_text observed=2026-08-06T13:07:27.400445Z digest=sha256:a7f05920fc240af8a4b81c9b13d820948caa1e144b5f8839493da161a6fe150b

Observation 841a32a5-75fb-44ab-9e85-cda3e624f931 · outbound

This paper cites Deformable DETR: Deformable Transformers for End-to-End Object Detection.

Tracking Moose using Aerial Object Detection Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 44

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source=pdf_text observed=2026-08-06T13:07:27.403936Z digest=sha256:88958a0cb2ff86c68f9ea7203e427c5467504b097a130f845d89cd403686f443

Observation f0fbf837-1c47-4231-b8cb-fc059fd0881b · outbound

This paper cites Depth-Wise Convolutions in Vision Transformers for Efficient Training on Small Datasets.

Tracking Moose using Aerial Object Detection Depth-Wise Convolutions in Vision Transformers for Efficient Training on Small Datasets

Reference 45

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local_arxiv, observed 2026-08-06T13:07:28.196046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 5207697c-22aa-4f96-8003-724e3fce0d22 · outbound

This paper cites A Lightweight CNN–Trans former Network With Laplacian Loss for Low-Altitude UA V Imagery Semantic Segmentation,.

Tracking Moose using Aerial Object Detection A Lightweight CNN–Trans former Network With Laplacian Loss for Low-Altitude UA V Imagery Semantic Segmentation,

Reference 46

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T13:07:27.410819Z digest=sha256:c5ad7ce420cae802c8866ee9b25702a07407dca867aee803a859da18a0d99518

Observation 629f2e32-49d9-4969-b14f-c1687914583e · outbound

This paper cites [Online].

Tracking Moose using Aerial Object Detection [Online]

Reference 47

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raw_fallback, observed 2026-08-06T13:07:29.243280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T13:07:27.413927Z digest=sha256:87effefa90306c11199cab90fc12fda0561f056e79ea664e8514690b80a99e8a

Observation 8fd2a0b2-4bf4-45ac-976a-d1920874b4b2 · outbound

This paper cites Dcef 2-yolo: Aerial detection yolo with deformable convolution–efficient feature fusion for small target dete ction,.

Tracking Moose using Aerial Object Detection Dcef 2-yolo: Aerial detection yolo with deformable convolution–efficient feature fusion for small target dete ction,

Reference 48

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 9df1e16f-bd9e-4001-9c5a-58ca5c1b648d · outbound

This paper cites ESOD: Efficient Small Object Detection on High-Resolution Images.

Tracking Moose using Aerial Object Detection ESOD: Efficient Small Object Detection on High-Resolution Images

Reference 49

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local_arxiv, observed 2026-08-06T13:07:28.104678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T13:07:27.419952Z digest=sha256:b69e694ddb5851af3ab01c86deababa78029f10a0e907b7e0e8df3125dfc9f19

Observation 8929f092-226b-47f6-8348-8736c898e9b6 · outbound

This paper cites D etection and tracking meet drones challenge,.

Tracking Moose using Aerial Object Detection D etection and tracking meet drones challenge,

Reference 50

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T13:07:27.423408Z digest=sha256:c883b6695077b5a3cd05d6238ce503237cd6aec932a5acf4c4ff7485dc7f2ac8

Observation 222a12cf-08d8-483b-b2f5-c503e6c96553 · outbound

This paper cites Patch-Level Augmentation for Object Detection in Aerial Images,.

Tracking Moose using Aerial Object Detection Patch-Level Augmentation for Object Detection in Aerial Images,

Reference 51

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no resolver link, observed 2026-08-06T13:07:27.426730Z

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source=pdf_text observed=2026-08-06T13:07:27.426730Z digest=sha256:ae0eb8b5b56a204caa0962ef4071bc920535d8cf13ee5a60a558198427b8bc98

Observation 24d4aeb4-3fe9-44ff-be56-ba395e84ee12 · outbound

This paper cites YOLOv11: An Overview of the Key Architectural Enhancements.

Tracking Moose using Aerial Object Detection YOLOv11: An Overview of the Key Architectural Enhancements

Reference 52

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source=pdf_text observed=2026-08-06T13:07:27.429798Z digest=sha256:cafcdc57b33bbde350f15f59b73140824a66da8b93d8a3263d07b3c59a0a99e6

Observation 83fec947-0cad-4589-8e19-b17bc46d8e86 · outbound

This paper cites moose detect Dataset,.

Tracking Moose using Aerial Object Detection moose detect Dataset,

Reference 53

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raw_fallback, observed 2026-08-06T13:07:29.233516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T13:07:27.432921Z digest=sha256:aedb74d19e6bc63e2f23ff727a5e9b553c3abe7bd63ebc6b4b4f6f778d3c869b

Observation 2f4cd5f0-0898-40fe-a3c9-61f1db216acc · outbound

This paper cites moose Dataset ,.

Tracking Moose using Aerial Object Detection moose Dataset ,

Reference 54

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raw_fallback, observed 2026-08-06T13:07:29.223586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T13:07:27.436207Z digest=sha256:e2124661840e94b92bd832ffbda332ad5dcebe10622eab41b6b156cfdb4f88f4

Observation 6a0c509d-9b94-4673-aa2e-f0ed38322700 · outbound

This paper cites moose Dataset ,.

Tracking Moose using Aerial Object Detection moose Dataset ,

Reference 55

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raw_fallback, observed 2026-08-06T13:07:29.213533Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T13:07:27.439938Z digest=sha256:3ad281a6533df14e988697e08d9ebe77407ff9da2da7d9ddbd16033a9efc90f7

Observation c1702c88-02d0-41e6-ad94-8437c303fb80 · outbound

This paper cites Moose detection Dataset ,.

Tracking Moose using Aerial Object Detection Moose detection Dataset ,

Reference 56

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raw_fallback, observed 2026-08-06T13:07:29.203180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 80f74b3e-531b-4837-b3dd-7382de0116ff · outbound

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

Tracking Moose using Aerial Object Detection YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 57

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source=pdf_text observed=2026-08-06T13:07:27.446178Z digest=sha256:5e19f9b48ea816e823d1cb8b419d6390bdb4ea8e53da41cb29b01643d8aa9c51

Observation b735d626-cc0f-4ba8-a279-d4c7b1588433 · outbound

This paper cites YOLO Evolution: A Comprehensive Benchmark and Architectural Review of YOLOv12, YOLO11, and Their Previous Versions.

Tracking Moose using Aerial Object Detection YOLO Evolution: A Comprehensive Benchmark and Architectural Review of YOLOv12, YOLO11, and Their Previous Versions

Reference 58

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source=pdf_text observed=2026-08-06T13:07:27.449662Z digest=sha256:0197ead311b17116671045a73ceef367419ddde22d2476498877c51064fc67e1

Observation c04b4404-d5a1-48a5-b6f6-d4afa307b462 · outbound

This paper cites Integrating remote sensing and deep le arning into aerial survey of large african mammals,.

Tracking Moose using Aerial Object Detection Integrating remote sensing and deep le arning into aerial survey of large african mammals,

Reference 59

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raw_fallback, observed 2026-08-06T13:07:27.918795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T13:07:27.452891Z digest=sha256:eb0dc801145c2501c2509efa605f474469082cc880ec5e6e9e0b138e645142c9

Observation f88fca8b-909b-4932-922b-79c78faa1d3b · outbound

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

Tracking Moose using Aerial Object Detection End-to-End Object Detection with Transformers

Reference 60

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source=pdf_text observed=2026-08-06T13:07:27.457164Z digest=sha256:e7fe2da7186f27caa0735d08f9489b006f5eb1453de26c2f8472a045a3da8446

Observation 080f3ce8-aad9-46ac-887c-2e7bcca026ec · outbound

This paper cites OpenMMLab Detection Tool box and Benchmark,.

Tracking Moose using Aerial Object Detection OpenMMLab Detection Tool box and Benchmark,

Reference 61

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raw_fallback, observed 2026-08-06T13:07:29.193381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T13:07:27.460841Z digest=sha256:18971936ad2bf8e1fe70d934dc74c49dfbf0e4667c7179becda16a499668ddba

Observation fb5dc5b6-0b1a-4bf9-95f8-74f83f5bb677 · outbound

This paper cites Deep Residual Learni ng for Image Recognition,.

Tracking Moose using Aerial Object Detection Deep Residual Learni ng for Image Recognition,

Reference 62

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T13:07:27.464202Z digest=sha256:734535c05ba88e6716871a6e48a1e0bda82d44755dae6db2451ed87f9ae41b19

Observation 9dc13a65-8905-4df3-8949-1b69d8362b68 · outbound

This paper cites ISBN 979835030 7184 pp.

Tracking Moose using Aerial Object Detection ISBN 979835030 7184 pp

Reference 2023

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source=pdf_text observed=2026-08-06T13:07:26.420706Z digest=sha256:886d94674e59b868a89474154b4e5e673351a288b0d1380dc12edc93b6952390

Pith citing papers

No inbound Pith citation observations are available.