Paper Citation Record · LEDGER

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data

As of 21 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 2 inbound Pith citation observations for arXiv:2411.15602.

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

pith.paper-citation-record.v1
2411.15602 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:09:00.827438Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:43:14.344541Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-09T17:43:14.553498Z

Reference resolution

23 of 23 outbound references displayed

verified exact2
verified fuzzy11
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d79da52a-7138-47b3-a7cb-a0fe17d69c8e · outbound

This paper cites Modern applications of evolutionary rule-based machine learning,.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Modern applications of evolutionary rule-based machine learning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:09:01.283097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 829080da-26d7-4eaa-94fb-9d7b08a7f0e7 · outbound

This paper cites Lateralized learning for robustness against adversarial attacks in a visual classification system,.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Lateralized learning for robustness against adversarial attacks in a visual classification system,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:09:01.265512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1813602f-d189-45eb-a638-618f373499ca · outbound

This paper cites Computer vision and deep learning for fish classification in underwater habitats: A survey,.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Computer vision and deep learning for fish classification in underwater habitats: A survey,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T14:09:00.743750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96701ab1-ac56-4b27-a7b2-64cc3d380d9b · outbound

This paper cites Lateralized learning to solve complex problems,.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Lateralized learning to solve complex problems,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:09:01.228009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 958896c9-edb8-4c2a-9715-695f4f011bfd · outbound

This paper cites Identifying the species of harvested tuna and billfish using deep convolutional neural networks,.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Identifying the species of harvested tuna and billfish using deep convolutional neural networks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:09:01.213007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c8af163b-8636-4a60-9606-fbb5f22ea28f · outbound

This paper cites Lateralized Learning for Multi-Class Visual Classification Tasks.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Lateralized Learning for Multi-Class Visual Classification Tasks

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:09:01.003233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2ed65f84-90e4-4d2e-9d48-5772761511ff · outbound

This paper cites A survey of graph neural networks in real world: Imbalance, noise, privacy and ood challenges,.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data A survey of graph neural networks in real world: Imbalance, noise, privacy and ood challenges,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T14:09:00.761126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fe74eeb-f8e4-4a10-bfe2-7a02968a336e · outbound

This paper cites A review on handling imbalanced data,.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data A review on handling imbalanced data,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:09:01.193765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fc6e8ba2-c253-4cdf-8be1-b64132c13a07 · outbound

This paper cites Accuracy of a smartphone-based object detection model, plantvillage nuru, in identifying the foliar symptoms of the viral diseases of cassava– cmd and cbsd,.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Accuracy of a smartphone-based object detection model, plantvillage nuru, in identifying the foliar symptoms of the viral diseases of cassava– cmd and cbsd,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:09:01.176999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ed1faf43-7b1b-4e3d-9158-b0df4f07b6c1 · outbound

This paper cites an unresolved cited work.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:09:01.161650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1baae3f6-5c81-4dcc-b892-9a1d5b5d9c54 · outbound

This paper cites Synsys: A synthetic data generation system for healthcare applications,.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Synsys: A synthetic data generation system for healthcare applications,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:09:01.146020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 57fe11d9-2cb0-4fcc-8bfa-567275047d50 · outbound

This paper cites Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T14:09:00.781249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fa97b76-677c-47fa-9a48-a1694bf72700 · outbound

This paper cites Synthetic data in machine learning for medicine and healthcare,.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Synthetic data in machine learning for medicine and healthcare,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:09:01.129049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3f656de3-a3a8-4f74-b1cc-33c4eaef434e · outbound

This paper cites Review and analysis of synthetic dataset generation methods and techniques for application in computer vision,.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Review and analysis of synthetic dataset generation methods and techniques for application in computer vision,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T14:09:00.790146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acecccae-044a-4f69-bd15-77abe187b133 · outbound

This paper cites Review of deep learning: concepts, cnn architectures, challenges, applications, future directions,.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Review of deep learning: concepts, cnn architectures, challenges, applications, future directions,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T14:09:00.793899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50a71fa2-6aa5-4c08-8671-d935695514d9 · outbound

This paper cites Multi-view 3d object detection network for autonomous driving,.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Multi-view 3d object detection network for autonomous driving,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T14:09:00.797985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e30493eb-ac21-4cd7-972f-74af1c20079f · outbound

This paper cites A review of yolo algorithm developments,.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data A review of yolo algorithm developments,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:09:01.072626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9254de85-79e5-4745-b444-1f507486156b · outbound

This paper cites Unity Perception: Generate Synthetic Data for Computer Vision.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Unity Perception: Generate Synthetic Data for Computer Vision

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T14:09:00.805859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52bf50f8-d2e3-42cd-88a8-375f389fa0cc · outbound

This paper cites Airsim: High-fidelity visual and physical simulation for autonomous vehicles,.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Airsim: High-fidelity visual and physical simulation for autonomous vehicles,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T14:09:00.810090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cfc110a-d917-48ad-af77-09b30a4e2749 · outbound

This paper cites Playing for data: Ground truth from computer games,.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Playing for data: Ground truth from computer games,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T14:09:00.813936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4592592-e49b-455d-acb8-fc4ee5912a96 · outbound

This paper cites From virtual to reality: Fast adaptation of virtual object detectors to real domains.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data From virtual to reality: Fast adaptation of virtual object detectors to real domains

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:09:01.039737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a9fc2ecf-ae78-4a21-adcf-c310c868215d · outbound

This paper cites Virtual and real world adaptation for pedestrian detection,.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Virtual and real world adaptation for pedestrian detection,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:09:01.018261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bad04eb1-98b2-4c48-aaf4-17c229192441 · outbound

This paper cites Training Constrained Deconvolutional Networks for Road Scene Semantic Segmentation.

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data Training Constrained Deconvolutional Networks for Road Scene Semantic Segmentation

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:09:00.877610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 7c155464-8092-4c2e-837c-19a263ad3e1f · inbound

OOD Detection with immature Models cites this paper.

OOD Detection with immature Models Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-08-09T17:43:14.558632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation afeb4676-4a03-4eb8-ac8e-e11ec30e0011 · inbound

Object Detection for Autonomous Driving in Chinese Rural Scenes: An Experimental Study on Real-Synthetic Data Mixing and Model Evaluation cites this paper.

Object Detection for Autonomous Driving in Chinese Rural Scenes: An Experimental Study on Real-Synthetic Data Mixing and Model Evaluation Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-30T12:27:40.590747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.