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Paper Citation Record · LEDGER

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

As of 15 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.

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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

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External citation measurements

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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

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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

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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

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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

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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

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

Reference 6

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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

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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

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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

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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

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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

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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

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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

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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

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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

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

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

Reference 16

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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

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

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

Reference 18

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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

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

Reference 20

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

Reference 21

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

Reference 22

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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

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

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

Reference 24

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

Reference 25

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

Reference 26

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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

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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

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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

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

Reference 30

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

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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

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

Reference 32

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

Reference 33

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

Reference 34

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

Reference 35

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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

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