Paper Citation Record · LEDGER

SL-YOLO: A Stronger and Lighter Drone Target Detection Model

As of 15 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2411.11477.

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

pith.paper-citation-record.v1
2411.11477 v3

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:31:30.706641Z

measured 42 of 42 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:35:46.906078Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T00:35:47.162260Z

Reference resolution

41 of 41 outbound references displayed

verified exact0
verified fuzzy29
unresolved12
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a17f00ef-c513-43a6-97e0-9352316b80c3 · outbound

This paper cites Yolov4: Optimal speed and accuracy of object detection, 2020.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Yolov4: Optimal speed and accuracy of object detection, 2020

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:31.372117Z

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.

Observation 08d441b2-d72c-44c9-b30a-daaffe3373b0 · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Xception: Deep learning with depthwise separable convolutions

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:31.356616Z

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.

Observation 3ba3b50c-2977-416a-9620-4b35d6e09961 · outbound

This paper cites Repvgg: Making vgg-style convnets great again.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Repvgg: Making vgg-style convnets great again

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:31.339933Z

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.

Observation 0b52d836-e65f-4d90-a965-15994ae20fd7 · outbound

This paper cites Visdrone-det2019: The vision meets drone ob- ject detection in image challenge results.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Visdrone-det2019: The vision meets drone ob- ject detection in image challenge results

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:31.322628Z

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.

Observation bc64bc05-e1ff-4ad9-a24f-ac35908574fb · outbound

This paper cites The pascal visual object classes challenge: A retrospective.IJCV, 111:98–136, 2015.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model The pascal visual object classes challenge: A retrospective.IJCV, 111:98–136, 2015

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:31.305253Z

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.

Observation d5ce2c9c-e0bb-4e96-86c1-218dda35f4b6 · outbound

This paper cites Yolov3: An incre- mental improvement.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Yolov3: An incre- mental improvement

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:31.288181Z

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.

Observation 8381fb64-e2c0-47e2-9044-9aaf3b12addd · outbound

This paper cites Dropblock: A regularization method for convolutional networks.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Dropblock: A regularization method for convolutional networks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:31.269903Z

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.

Observation 9fa9abe3-6867-406c-9a73-c8db9ee444c8 · outbound

This paper cites Nas-fpn: Learning scalable feature pyramid architecture for object de- tection.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Nas-fpn: Learning scalable feature pyramid architecture for object de- tection

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:31.252677Z

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.

Observation 4f5f4ec4-151d-400f-9355-7193726b888d · outbound

This paper cites Fast r-cnn.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Fast r-cnn

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:31.233125Z

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.

Observation 0aa6c5b5-3067-48a2-bef7-024cbd079b8e · outbound

This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Rich feature hierarchies for accurate object detection and semantic segmentation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:31.217030Z

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.

Observation c8a05858-d80f-4681-9aa5-44e45f6c4971 · outbound

This paper cites Ghostnet: More features from cheap opera- tions.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Ghostnet: More features from cheap opera- tions

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:31.200722Z

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.

Observation 9d44d5e1-7ae4-4402-827e-faec8a794292 · outbound

This paper cites Deep residual learning for image recognition.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Deep residual learning for image recognition

Reference 12

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Source-reported events for the cited work

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Observation 4bd57904-92a1-4dec-868d-e42ea1186dbd · outbound

This paper cites Searching for mo- bilenetv3.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Searching for mo- bilenetv3

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:31.172664Z

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.

Observation e8a50e62-8d7a-4310-ab8e-9279b4c58511 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T18:31:30.575464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 593de1fd-2008-41a2-bcca-30da685a57eb · outbound

This paper cites Densely connected convolutional net- works.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Densely connected convolutional net- works

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T18:31:30.580683Z

Source-reported events for the cited work

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Observation 6968a012-c879-40b6-abe6-64198587b566 · outbound

This paper cites Ultralytics yolo.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Ultralytics yolo

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:31.144700Z

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.

Observation 8285b822-62e3-4c43-93dd-b511cbde3136 · outbound

This paper cites YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T18:31:30.591280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03d8e91a-d0a2-4447-bcb2-1ed9f3304efd · outbound

This paper cites Microsoft coco: Common objects in context.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Microsoft coco: Common objects in context

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T18:31:30.596557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 131f46a9-5a3e-418e-aba7-1204bcf350f7 · outbound

This paper cites Feature pyramid networks for object detection.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Feature pyramid networks for object detection

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T18:31:30.601220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5d884fc-e646-4660-8ad5-725743bc8726 · outbound

This paper cites Path aggregation network for instance segmentation.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Path aggregation network for instance segmentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:31.107449Z

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.

Observation bcd751cb-5a09-4131-ba83-49757f06812e · outbound

This paper cites Learning Spatial Fusion for Single-Shot Object Detection.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Learning Spatial Fusion for Single-Shot Object Detection

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T18:31:30.611168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7860262-4abb-4aa8-9d98-fa9cabaf7c09 · outbound

This paper cites Fully convolutional networks for semantic segmentation.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Fully convolutional networks for semantic segmentation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T18:31:30.617013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec2fb24c-d081-4feb-865b-293deb7fe22c · outbound

This paper cites Shufflenet v2: Practical guidelines for efficient cnn architec- ture design.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Shufflenet v2: Practical guidelines for efficient cnn architec- ture design

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:31.077773Z

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.

Observation 41f011dd-d639-468b-b463-a03eb80c6291 · outbound

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

SL-YOLO: A Stronger and Lighter Drone Target Detection Model You only look once: Unified, real-time object detection

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:31.060991Z

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.

Observation 4493d3c5-a551-4d54-98fd-2f5c2fb20fb6 · outbound

This paper cites Yolo9000: better, faster, stronger.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Yolo9000: better, faster, stronger

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:31.043168Z

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.

Observation 900cd782-98aa-42e7-b6f1-1cbb869b1a62 · outbound

This paper cites Faster R-CNN: Towards real-time object detection with re- gion proposal networks.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Faster R-CNN: Towards real-time object detection with re- gion proposal networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:31.025454Z

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.

Observation 8a5224c6-ce39-4c1c-8103-4ff9475ea562 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T18:31:30.640309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 385cc29f-5f56-40f0-ae5a-cb0d7f2aea47 · outbound

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

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T18:31:30.644979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 348db5fa-bf10-4800-afba-e7fbb84de315 · outbound

This paper cites Efficientnetv2: Smaller models and faster training.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Efficientnetv2: Smaller models and faster training

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:30.987147Z

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.

Observation 76a21216-1dce-424d-a64e-c275ba949f10 · outbound

This paper cites Efficient- det: Scalable and efficient object detection.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Efficient- det: Scalable and efficient object detection

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:30.970891Z

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.

Observation 468111d5-ed54-422c-bcc8-5b8b2ad0f612 · outbound

This paper cites A survey of object detection for uavs based on deep learning.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model A survey of object detection for uavs based on deep learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:30.954429Z

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.

Observation fc016d1e-d5d1-4def-aef5-99432ed27574 · outbound

This paper cites Attention is all you need.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Attention is all you need

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T18:31:30.663937Z

Source-reported events for the cited work

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Observation 3e0975c8-74e4-4f01-9719-10d7453544e7 · outbound

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

SL-YOLO: A Stronger and Lighter Drone Target Detection Model YOLOv10: Real-Time End-to-End Object Detection

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T18:31:30.668564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91acb36b-0800-47c9-8bb2-24f927da3eeb · outbound

This paper cites Cspnet: A new backbone that can enhance learning capability of cnn.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Cspnet: A new backbone that can enhance learning capability of cnn

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:30.927131Z

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.

Observation 0103bb2e-163d-4acc-9598-1fd57b635f3e · outbound

This paper cites Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:30.909651Z

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.

Observation bddc000a-c842-427a-ac78-5204d493555b · outbound

This paper cites Yolov9: Learning what you want to learn using pro- grammable gradient information, 2024.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Yolov9: Learning what you want to learn using pro- grammable gradient information, 2024

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:30.892139Z

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.

Observation 346bc631-203e-457b-a756-f85f51e33aa2 · outbound

This paper cites Deep learning for unmanned aerial vehicle-based object de- tection and tracking: A survey.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Deep learning for unmanned aerial vehicle-based object de- tection and tracking: A survey

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:30.874274Z

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.

Observation 4105a159-2389-4d78-ab72-1f82e3411cdf · outbound

This paper cites Aggregated residual transformations for deep neural networks.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Aggregated residual transformations for deep neural networks

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:30.858222Z

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.

Observation 7334f607-1228-4d6b-8466-dd703ae85fb6 · outbound

This paper cites Cutmix: Regu- larization strategy to train strong classifiers with localizable features.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Cutmix: Regu- larization strategy to train strong classifiers with localizable features

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:30.842483Z

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.

Observation ad66c059-e404-47f4-983e-b4470d66e9fb · outbound

This paper cites Shufflenet: An extremely efficient convolutional neural net- work for mobile devices.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Shufflenet: An extremely efficient convolutional neural net- work for mobile devices

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:30.826495Z

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.

Observation 86aab76f-aaf3-4746-8526-012e56ec00d8 · outbound

This paper cites Distance-iou loss: Faster and bet- ter learning for bounding box regression.

SL-YOLO: A Stronger and Lighter Drone Target Detection Model Distance-iou loss: Faster and bet- ter learning for bounding box regression

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:31:30.810307Z

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.

Pith citing papers

Observation 1b866edd-6765-46a2-9f01-a8fa36079b8b · inbound

A Review of Vision-Based Vehicle Detection for UAV-Based Traffic Monitoring: Experimental Insights and Future Directions cites this paper.

A Review of Vision-Based Vehicle Detection for UAV-Based Traffic Monitoring: Experimental Insights and Future Directions SL-YOLO: A Stronger and Lighter Drone Target Detection Model

Reference 99

Resolution: verified exact; local_arxiv, observed 2026-08-11T00:35:47.166649Z

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.