Paper Citation Record · LEDGER

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models

As of 12 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2501.08639.

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

pith.paper-citation-record.v1
2501.08639 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:25:43.142503Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T20:24:01.961913Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T20:27:22.549435Z

Reference resolution

36 of 36 outbound references displayed

verified exact0
verified fuzzy26
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fb8dd669-4a98-47e6-913d-447462730037 · outbound

This paper cites Increasing damages from wildfires warrant investment in wildland fire management,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models Increasing damages from wildfires warrant investment in wildland fire management,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.655742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dda997c8-3cb9-4d9a-b3a4-0d54d4cccdd8 · outbound

This paper cites A wildfire smoke detection system using unmanned aerial vehicle images based on the optimized YOLOv5,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models A wildfire smoke detection system using unmanned aerial vehicle images based on the optimized YOLOv5,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.640952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 01bf0aa5-8d23-488b-8124-919ea194b745 · outbound

This paper cites A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.627366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 013ac05d-3e2f-4dce-aacd-d8ebf8692f16 · outbound

This paper cites Near-edge computing aware object detection: a review,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models Near-edge computing aware object detection: a review,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.614325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6f348aa9-b301-467e-8ef7-0fa181f31b8e · outbound

This paper cites UAV based cost -effective real -time abnormal event detection using edge computing,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models UAV based cost -effective real -time abnormal event detection using edge computing,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.600219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e54ab70f-f801-450b-80a1-e324bfe88f98 · outbound

This paper cites Challenges in energy- efficient deep neural network training with FPGA,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models Challenges in energy- efficient deep neural network training with FPGA,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.588170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 62ef023a-6379-4cc2-a47c-dcb0472f016d · outbound

This paper cites YOLOv5 Ultralytics,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models YOLOv5 Ultralytics,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.575749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ab4a39f3-7337-4005-8e67-20725a1c5b34 · outbound

This paper cites Transfer learning enhanced deep learning model for wildfire flame and smoke detection,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models Transfer learning enhanced deep learning model for wildfire flame and smoke detection,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.556347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 18d0a5f9-dc4c-4004-bc2d-36e1b7daf332 · outbound

This paper cites FASDD: an open-access 100,000-level flame and smoke detection dataset for deep learning in fire detection,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models FASDD: an open-access 100,000-level flame and smoke detection dataset for deep learning in fire detection,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.541671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5563d0b4-23eb-4445-a73b-27d24ece5031 · outbound

This paper cites An automatic fire detection system based on deep convolutional neural networks for low - power, resource-constrained devices,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models An automatic fire detection system based on deep convolutional neural networks for low - power, resource-constrained devices,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.528566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 18ee9ac1-64d3-4a11-b599-1bcca817de5b · outbound

This paper cites Aerial imagery pile burn detection using deep learning: The FLAME dataset,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models Aerial imagery pile burn detection using deep learning: The FLAME dataset,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.516652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 86f44175-dd72-429b-a01a-b15270878c34 · outbound

This paper cites Wildland fire detection and monitoring using a drone-collected rgb/ir image dataset,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models Wildland fire detection and monitoring using a drone-collected rgb/ir image dataset,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.505667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1d8305c7-3787-4c31-8714-26382ebc853c · outbound

This paper cites The evolution of object detection methods,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models The evolution of object detection methods,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.493754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c71e80eb-901c-44f0-a71c-cbcbb3ec189d · outbound

This paper cites Bibliometric analysis of one -stage and two -stage object detection,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models Bibliometric analysis of one -stage and two -stage object detection,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.482186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c95b8c19-30ad-4d32-b46b-6cb2739d44a0 · outbound

This paper cites Cascade R -CNN: high quality object detection and instance segmentation,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models Cascade R -CNN: high quality object detection and instance segmentation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.471992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3c6c43a4-5eb8-471f-92f7-b2ad2e47dd0d · outbound

This paper cites Dynamic R-CNN: towards high quality object detection via dynamic training,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models Dynamic R-CNN: towards high quality object detection via dynamic training,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.460611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d6163086-2db0-478a-9be4-9b947b6bd215 · outbound

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

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models Faster R-CNN: towards real-time object detection with region proposal networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.448087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5019cfd2-e7d8-423e-b86c-d87064e4b1cf · outbound

This paper cites RTMDet: An Empirical Study of Designing Real-Time Object Detectors.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models RTMDet: An Empirical Study of Designing Real-Time Object Detectors

Reference 18

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Source-reported events for the cited work

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Observation 28f3663f-8275-46ea-b634-0536eb223691 · outbound

This paper cites TOOD: Task-aligned One-stage Object Detection.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models TOOD: Task-aligned One-stage Object Detection

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T20:25:43.062734Z

Source-reported events for the cited work

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Observation 8d62a770-65ca-4530-a6ae-2f8b87599a15 · outbound

This paper cites DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T20:25:43.067110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb7b3042-8bb0-44b4-9375-3417df44dd86 · outbound

This paper cites DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T20:25:43.071574Z

Source-reported events for the cited work

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Observation cd63fa1e-d9d3-4655-be20-21b8f1bca561 · outbound

This paper cites DN-DETR: Accelerate DETR Training by Introducing Query DeNoising.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T20:25:43.076119Z

Source-reported events for the cited work

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Observation 62cc14a6-337a-4d81-9995-f83f40b8823d · outbound

This paper cites Comparing YOLOv3, YOLOv4 and YOLOv5 for autonomous landing spot detection in faulty UAVs,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models Comparing YOLOv3, YOLOv4 and YOLOv5 for autonomous landing spot detection in faulty UAVs,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.432451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1e3ccbcb-8507-4583-b701-4a125f31b837 · outbound

This paper cites Assessing the effectiveness of YOLO architectures for smoke and wildfire detection,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models Assessing the effectiveness of YOLO architectures for smoke and wildfire detection,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.419407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 48846d4e-8abf-4dc6-9b91-aec4527e9c0e · outbound

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

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

Reference 25

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Source-reported events for the cited work

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Observation 13e03d26-274b-45d7-9a00-be227baf7529 · outbound

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

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models YOLOv10: Real-Time End-to-End Object Detection

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T20:25:43.092522Z

Source-reported events for the cited work

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Observation ea6ea41c-07fd-4525-bbaf-ee70bce6d7d0 · outbound

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

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models YOLO Evolution: A Comprehensive Benchmark and Architectural Review of YOLOv12, YOLO11, and Their Previous Versions

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T20:25:43.096680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 663420dd-035d-4d16-aa0c-7d723fe11e35 · outbound

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

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models Microsoft COCO: common objects in context,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.407407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c28af8f8-a1b7-4496-9711-d1eb8e96f28b · outbound

This paper cites What is average precision in object detection & localization algorithms and how to calculate it?.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models What is average precision in object detection & localization algorithms and how to calculate it?

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.394265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6029ee8c-9c63-44a4-a841-db5df3b22957 · outbound

This paper cites Chapter 15 - a framework for accelerating bottlenecks in GPU execution with assist warps,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models Chapter 15 - a framework for accelerating bottlenecks in GPU execution with assist warps,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.380070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e2f615d9-fb47-4a04-9a75-a4a6f81ae682 · outbound

This paper cites Research on the influence of the depth and width of YOLOv5 network structure on taffic signal detection performance,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models Research on the influence of the depth and width of YOLOv5 network structure on taffic signal detection performance,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.367685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7d53046a-da2b-4834-ab30-a152e256e193 · outbound

This paper cites YOLOv6 v3.0: A Full-Scale Reloading.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models YOLOv6 v3.0: A Full-Scale Reloading

Reference 32

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Source-reported events for the cited work

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Observation 2033e9e2-d0d2-4834-8e35-042392ef1c88 · outbound

This paper cites Ultralytics YOLO ,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models Ultralytics YOLO ,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.354984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 04685100-7e7e-4c40-8994-a24413c460b5 · outbound

This paper cites Ultralytics YOLO 11,.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models Ultralytics YOLO 11,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.342291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4fdcd672-d289-49ca-9e09-7fddba75d3ff · outbound

This paper cites MMDetection: Open MMLab Detection Toolbox and Benchmark.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models MMDetection: Open MMLab Detection Toolbox and Benchmark

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T20:25:43.134769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f2f5fa1-f1a3-4b19-b7d0-45f716ad1b8d · outbound

This paper cites A gentle introduction to k-fold cross-validation.

Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models A gentle introduction to k-fold cross-validation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:25:43.318718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation b08adbb5-9361-4d19-adba-9f1363411073 · inbound

Belief-Aware Scheduling for Predictive Wildfire Hazard Mapping under Sparse-Window Telemetry cites this paper.

Belief-Aware Scheduling for Predictive Wildfire Hazard Mapping under Sparse-Window Telemetry Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-02T20:27:22.551475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.