Paper Citation Record · LEDGER

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges

As of 16 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2508.02067.

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

pith.paper-citation-record.v1
2508.02067 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:15:43.142692Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-23T01:43:12.464857Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T01:45:18.363741Z

Reference resolution

40 of 40 outbound references displayed

verified exact2
verified fuzzy23
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4b25333d-e22f-494f-ada5-55e91c063b7b · outbound

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

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Rich feature hierarchies for accurate object detection and semantic segmentation,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:44.186712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bfa631c0-09d8-4367-b6f6-5c211c54900b · outbound

This paper cites Fast r-cnn,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Fast r-cnn,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:44.160730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8c8fd7b9-7e36-4393-9d47-9f6275fed9cf · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Faster r-cnn: Towards real-time object detection with region proposal networks,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T05:15:41.926887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3365a331-abfc-4b52-9964-792ad93839cd · outbound

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

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges You only look once: Unified, real-time object detection,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:44.118481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b35f7424-82de-4685-8cd8-5bf20cf0109d · outbound

This paper cites Discriminatively trained deformable part models, release 1,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Discriminatively trained deformable part models, release 1,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:44.091928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c65500cf-e25b-4243-ad15-8a327b212def · outbound

This paper cites Se- lective search for object recognition,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Se- lective search for object recognition,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:44.063758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 152dc88d-bd2f-497d-9426-ec25b0b25f87 · outbound

This paper cites Ssd: Single shot multibox detector,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Ssd: Single shot multibox detector,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T05:15:42.041377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 156d666a-c5e6-4273-9bb9-1a5895090761 · outbound

This paper cites Going deeper with convolutions,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Going deeper with convolutions,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T05:15:42.071044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95576dc5-6cba-4b90-808d-4f46ff599808 · outbound

This paper cites Yolo9000: Better, faster, stronger,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Yolo9000: Better, faster, stronger,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:44.023103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ad6483d6-4874-4550-9a8a-d7fc7058bac0 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:43.996159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0596d39d-8d03-4a6c-8c95-877dd66a1cde · outbound

This paper cites YOLOv3: An Incremental Improvement.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges YOLOv3: An Incremental Improvement

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T05:15:42.161362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b863217-7df0-45cc-b70b-06f2fdfeab72 · outbound

This paper cites Deep residual learning for image recognition,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Deep residual learning for image recognition,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T05:15:42.204837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 165faf73-61a7-4c4a-8428-deeb652ca9c0 · outbound

This paper cites Focal loss for dense object detection,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Focal loss for dense object detection,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:43.963071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 66ef7014-5d59-4361-914a-ba698219c123 · outbound

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

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T05:15:42.275092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0348b434-aba2-4860-af79-69107e73c7eb · outbound

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

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Cspnet: A new backbone that can enhance learning capability of cnn,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:43.938578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 76735dc7-6d5a-4df7-9727-b13a8c976f4f · outbound

This paper cites Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:43.915199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f5ab99cf-bbda-4fdc-88f7-7c5d12e48522 · outbound

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

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Dropblock: A regularization method for convolutional networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:43.893920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ac335f87-ce03-490d-91c3-3691910635d5 · outbound

This paper cites Mish: A Self Regularized Non-Monotonic Activation Function.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Mish: A Self Regularized Non-Monotonic Activation Function

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T05:15:42.396390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed7ab9e7-aaa3-43ce-9984-2d693dae59c0 · outbound

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

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Spatial pyramid pooling in deep convolutional networks for visual recognition,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:43.865665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 52f40dc0-4354-466c-bc9c-d7f07d0a0875 · outbound

This paper cites Path aggregation network for instance segmentation,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Path aggregation network for instance segmentation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:43.832968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fbc3d595-9922-49f0-9a82-bfc1958f0fac · outbound

This paper cites ultralytics/yolov5: v1.0 - first release,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges ultralytics/yolov5: v1.0 - first release,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T05:15:42.482801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f761f03a-bd27-4db1-8bea-cc3e5e12a13f · outbound

This paper cites mixup: Beyond empirical risk minimization,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges mixup: Beyond empirical risk minimization,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T05:15:42.515997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fbd0aca-e3d3-4a45-a9c7-a88e64cc72c6 · outbound

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

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T05:15:42.553276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bccb482d-a474-408f-b600-9a0942ff04fb · outbound

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

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Repvgg: Making vgg-style convnets great again,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:43.808634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 262f79c4-79eb-44a0-9894-226c99aeed20 · outbound

This paper cites Fcos: Fully convolutional one-stage object detection,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Fcos: Fully convolutional one-stage object detection,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:43.786300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d97101f4-83ad-4a0a-b906-b63a355abc68 · outbound

This paper cites YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T05:15:42.658238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4b3e6d4-5857-462e-b3b5-defaa504abe9 · outbound

This paper cites Ultralytics yolov8,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Ultralytics yolov8,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:43.762997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c1a65794-ed55-43f4-9f96-3fe244ffbc8a · outbound

This paper cites Objects as Points.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Objects as Points

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T05:15:42.736080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b03c36f-8739-4e35-aa1c-219790e8c1ad · outbound

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

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T05:15:42.792878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c01a34a6-81b9-4ac9-b34a-9594a9cb0838 · outbound

This paper cites Efficientdet: Scalable and efficient object detection,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Efficientdet: Scalable and efficient object detection,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:43.734050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 06c0561a-2772-4858-b0e5-e738e9170e70 · outbound

This paper cites Ota: Optimal transport assignment for object detection,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Ota: Optimal transport assignment for object detection,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:43.704137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 68427eed-fa64-44c6-945b-2e9948cb0ce9 · outbound

This paper cites Yolov11: Release notes and model overview,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Yolov11: Release notes and model overview,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:43.675073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7e42515e-40f6-4675-839e-8ff1971e2506 · outbound

This paper cites Ultralytics yolov11 models: Comparison and performance,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Ultralytics yolov11 models: Comparison and performance,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:43.654466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 88eb9461-e6c5-43ef-8926-98882e1f3d8f · outbound

This paper cites Yolov11: Revolutionizing agricultural fruitlet detection with enhanced accuracy and real-time deployment,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Yolov11: Revolutionizing agricultural fruitlet detection with enhanced accuracy and real-time deployment,

Reference 34

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-06T05:15:43.479818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation de3e049f-d2de-4da0-8aa7-704af77dadef · outbound

This paper cites Domain adaptive yolo for cross-domain object detection,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Domain adaptive yolo for cross-domain object detection,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:43.631166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 94768042-6818-418c-b263-675f55bd61a2 · outbound

This paper cites Stac: Semi-supervised learning for object detection via strong-to-weak consistency,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Stac: Semi-supervised learning for object detection via strong-to-weak consistency,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:43.604753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7b802c87-2dd1-41d9-9594-9a6fe4825cdd · outbound

This paper cites Robust-yolo: Noise and occlusion aware object detection,.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Robust-yolo: Noise and occlusion aware object detection,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:15:43.575341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation df3cede8-681c-472a-bffa-402401aa5856 · outbound

This paper cites DDDM: a Brain-Inspired Framework for Robust Classification.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges DDDM: a Brain-Inspired Framework for Robust Classification

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-06T05:15:43.253346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ec9b9f7b-57e8-4025-83ab-c4e2b1bfcfa8 · outbound

This paper cites LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T05:15:43.126280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35b441f4-b625-4bbe-8f4c-9a1799cece08 · outbound

This paper cites Deep Semantic Statistics Matching (D2SM) Denoising Network.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Deep Semantic Statistics Matching (D2SM) Denoising Network

Reference 40

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T05:15:43.204587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

Observation c0c15770-124b-4ae6-a7ba-4806f029e80a · inbound

A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation cites this paper.

A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:45:18.365716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.