Paper Citation Record · LEDGER

From classical techniques to convolution-based models: A review of object detection algorithms

As of 16 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2412.05252.

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

pith.paper-citation-record.v1
2412.05252 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:53:28.680849Z

measured 36 of 36 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 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

36 of 36 outbound references displayed

verified exact0
verified fuzzy12
unresolved24
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ebf279dc-a281-4ac8-97be-d03e08467299 · outbound

This paper cites Review of image classification algorithms based on convolutional neural networks,.

From classical techniques to convolution-based models: A review of object detection algorithms Review of image classification algorithms based on convolutional neural networks,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:53:29.887099Z

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 f0450871-412c-4190-b64f-c15fde7541ec · outbound

This paper cites Investi- gations of object detection in images/videos using various deep learning techniques and embedded platforms—a comprehensive review,.

From classical techniques to convolution-based models: A review of object detection algorithms Investi- gations of object detection in images/videos using various deep learning techniques and embedded platforms—a comprehensive review,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:53:29.872957Z

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 32eb361a-675e-440f-a472-b1c633091fc0 · outbound

This paper cites Image matching from handcrafted to deep features: A survey,.

From classical techniques to convolution-based models: A review of object detection algorithms Image matching from handcrafted to deep features: A survey,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:53:29.782284Z

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 3ae08cee-c744-494d-b54b-34f0d0fd2aaf · outbound

This paper cites Distinctive image features from scale-invariant keypoints,.

From classical techniques to convolution-based models: A review of object detection algorithms Distinctive image features from scale-invariant keypoints,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.141926Z

Source-reported events for the cited work

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Observation 97f08378-86dc-4f4f-9bb0-0f1c92592fec · outbound

This paper cites A computational approach to edge detection,.

From classical techniques to convolution-based models: A review of object detection algorithms A computational approach to edge detection,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.145708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bea456fe-3442-4bc9-90df-141ee2534f46 · outbound

This paper cites Histograms of oriented gradients for human detection,.

From classical techniques to convolution-based models: A review of object detection algorithms Histograms of oriented gradients for human detection,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.190556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35180475-44e1-4f52-aa69-7a94aed9a4ab · outbound

This paper cites Rapid object detection using a boosted cascade of simple features,.

From classical techniques to convolution-based models: A review of object detection algorithms Rapid object detection using a boosted cascade of simple features,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:53:29.655630Z

Source-reported events for the cited work

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Observation dd4766d6-7cbb-4461-9da4-b1430388a6c5 · outbound

This paper cites A discriminatively trained, multiscale, deformable part model,.

From classical techniques to convolution-based models: A review of object detection algorithms A discriminatively trained, multiscale, deformable part model,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:53:29.640036Z

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 1ce64c4c-c069-4ac9-9ac0-dcfb9f3882e4 · outbound

This paper cites Accurate object detection with joint classification-regression random forests,.

From classical techniques to convolution-based models: A review of object detection algorithms Accurate object detection with joint classification-regression random forests,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:53:29.483490Z

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 18f3f4fe-faaa-4f5a-8b31-ae3950b0737b · outbound

This paper cites OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks.

From classical techniques to convolution-based models: A review of object detection algorithms OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.362298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a8ac2bb-4640-4903-9293-0b135ddb0631 · outbound

This paper cites Visual saliency based on multiscale deep features,.

From classical techniques to convolution-based models: A review of object detection algorithms Visual saliency based on multiscale deep features,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:53:29.468268Z

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 5c8e43b8-1699-4ca9-904a-5af40c6f541f · outbound

This paper cites Salient object detection via color contrast and color distribution,.

From classical techniques to convolution-based models: A review of object detection algorithms Salient object detection via color contrast and color distribution,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:53:29.453447Z

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 a75d839c-f8e1-4bad-bc6c-e818602757f9 · outbound

This paper cites Edge boxes: Locating object proposals from edges,.

From classical techniques to convolution-based models: A review of object detection algorithms Edge boxes: Locating object proposals from edges,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:53:29.277462Z

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 d15ed811-8cf2-489e-9c1d-e76b3c3d81e5 · outbound

This paper cites Superpixel based color contrast and color distribution driven salient object detection,.

From classical techniques to convolution-based models: A review of object detection algorithms Superpixel based color contrast and color distribution driven salient object detection,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:53:29.138286Z

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 b9854946-bef7-4181-bb93-bedcab0df34b · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

From classical techniques to convolution-based models: A review of object detection algorithms Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.382915Z

Source-reported events for the cited work

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Observation caaf5066-1b0f-4a6f-be66-8f88fd567300 · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

From classical techniques to convolution-based models: A review of object detection algorithms Imagenet classification with deep convolutional neural networks,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.387540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21765e54-52bf-49c1-9d40-ba549d148b5d · outbound

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

From classical techniques to convolution-based models: A review of object detection algorithms Rich feature hierarchies for accurate object detection and semantic segmentation,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.391491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3478e0db-3cca-4eae-8c1c-587d6eec2a7f · outbound

This paper cites Fast R-CNN.

From classical techniques to convolution-based models: A review of object detection algorithms Fast R-CNN

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.395235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cee5ee88-e5bc-4f90-9523-8300131dde72 · outbound

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

From classical techniques to convolution-based models: A review of object detection algorithms Faster r-cnn: Towards real-time object detection with region proposal networks,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.399896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fdb2c46-b275-4329-b334-2b9a082995ce · outbound

This paper cites Mask r-cnn,.

From classical techniques to convolution-based models: A review of object detection algorithms Mask r-cnn,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.404314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be6cf032-e641-421e-8500-6d8b6257fbf0 · outbound

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

From classical techniques to convolution-based models: A review of object detection algorithms You only look once: Unified, real-time object detection,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.408877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11311462-c4d2-460c-a8f7-9b14a863b8b2 · outbound

This paper cites Ssd: Single shot multibox detector,.

From classical techniques to convolution-based models: A review of object detection algorithms Ssd: Single shot multibox detector,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.413309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04b1c554-6bb4-4a48-9b59-c3ef1407b1ef · outbound

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

From classical techniques to convolution-based models: A review of object detection algorithms Spatial pyramid pooling in deep convolutional networks for visual recognition,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.417514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e95c8a2-a5e5-4ccc-af57-6edf98ae866c · outbound

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

From classical techniques to convolution-based models: A review of object detection algorithms Yolo9000: better, faster, stronger,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.421379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2df70e6-0cf6-47ef-a996-efd1a274fdb6 · outbound

This paper cites Yolov3: An incremental improvement,.

From classical techniques to convolution-based models: A review of object detection algorithms Yolov3: An incremental improvement,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.425725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b60dd588-aac4-4d14-85cb-4fe096d9312c · outbound

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

From classical techniques to convolution-based models: A review of object detection algorithms YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.429550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11f94691-da2c-423a-ba25-b94c82bbef86 · outbound

This paper cites ultralytics/yolov5: v6. 0-yolov5n’nano’models, roboflow integration, tensorflow export, opencv dnn support,.

From classical techniques to convolution-based models: A review of object detection algorithms ultralytics/yolov5: v6. 0-yolov5n’nano’models, roboflow integration, tensorflow export, opencv dnn support,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:53:29.046917Z

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 8d12da9e-30d4-4e0a-9c21-2bce77878e95 · outbound

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

From classical techniques to convolution-based models: A review of object detection algorithms YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.480516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d827041-51f0-4e6b-8f9b-cdae1f6fd174 · outbound

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

From classical techniques to convolution-based models: A review of object detection algorithms Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.526321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80312449-8d57-45c4-8c2c-e064e9287d10 · outbound

This paper cites Ultralytics yolov8,.

From classical techniques to convolution-based models: A review of object detection algorithms Ultralytics yolov8,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:53:28.945016Z

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 bd83bdca-93f3-4fcb-93bb-0092fc5b13e8 · outbound

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

From classical techniques to convolution-based models: A review of object detection algorithms YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.655703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af2a8a71-cbef-4964-82ae-a1b65d8f98e2 · outbound

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

From classical techniques to convolution-based models: A review of object detection algorithms YOLOv10: Real-Time End-to-End Object Detection

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.662446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c44b9a8-155b-435b-a86e-1b86fe98b490 · outbound

This paper cites The pascal visual object classes (voc) challenge,.

From classical techniques to convolution-based models: A review of object detection algorithms The pascal visual object classes (voc) challenge,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.667837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d22934b7-7dd2-42a6-87c6-b3cb815cf529 · outbound

This paper cites Microsoft coco: Common objects in context,.

From classical techniques to convolution-based models: A review of object detection algorithms Microsoft coco: Common objects in context,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.671904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f05466b-f715-4081-803c-3fef0da5f604 · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

From classical techniques to convolution-based models: A review of object detection algorithms Imagenet: A large-scale hierarchical image database,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.676484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6058365a-4156-4e24-b087-7e0d92281752 · outbound

This paper cites The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale,.

From classical techniques to convolution-based models: A review of object detection algorithms The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T20:53:28.680849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.