Paper Citation Record · LEDGER

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study

As of 18 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2506.07539.

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

pith.paper-citation-record.v1
2506.07539 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:35:20.661954Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T11:10:27.459471Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:39:42.338142Z

Reference resolution

32 of 32 outbound references displayed

verified exact1
verified fuzzy25
unresolved6
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1bea5b8e-94d1-4829-aeb0-a497863970c5 · outbound

This paper cites Towards sim-to-real industrial parts classification with synthetic dataset,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Towards sim-to-real industrial parts classification with synthetic dataset,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:21.020681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8ace7379-dfea-4ba1-8ee7-ba7c0dc3dc18 · outbound

This paper cites Deep learning methods for object detection in smart manufacturing: A survey,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Deep learning methods for object detection in smart manufacturing: A survey,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:21.008322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fabe68ed-8a96-4ac5-a11c-41dd2bdc216f · outbound

This paper cites Yolo-based object detec- tion models: A review and its applications,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Yolo-based object detec- tion models: A review and its applications,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.996749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f5a3d063-d0ef-4c65-8991-fa7d56ed7d7a · outbound

This paper cites Generating images with physics- based rendering for an industrial object detection task: Realism versus domain randomization,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Generating images with physics- based rendering for an industrial object detection task: Realism versus domain randomization,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.985751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 47b55883-0c90-471e-a391-7764fad60316 · outbound

This paper cites Automated assembly quality inspection by deep learning with 2d and 3d synthetic cad data,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Automated assembly quality inspection by deep learning with 2d and 3d synthetic cad data,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.975036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bc13124e-54f2-496b-abbe-ed6909115aa5 · outbound

This paper cites Domain randomization for transferring deep neural networks from simulation to the real world,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Domain randomization for transferring deep neural networks from simulation to the real world,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.564671Z

Source-reported events for the cited work

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Observation c9133d25-ac3f-4795-a959-5071cbe140e9 · outbound

This paper cites CAD-based Learning for Egocentric Object Detection in Industrial Context,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study CAD-based Learning for Egocentric Object Detection in Industrial Context,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.956920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f709be22-8dc4-4a36-ab9a-f81bb573a7fe · outbound

This paper cites Two-stage filtering method to improve the performance of object detection trained by synthetic dataset in heavily cluttered industry scenes,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Two-stage filtering method to improve the performance of object detection trained by synthetic dataset in heavily cluttered industry scenes,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.945143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a19cf860-753d-4567-974c-ace526d08011 · outbound

This paper cites A novel method for object detection using deep learning and CAD models.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study A novel method for object detection using deep learning and CAD models

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:35:20.718996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 45c1e0bd-5e2b-451a-9101-be5d43236743 · outbound

This paper cites Object detection using sim2real domain randomization for robotic applica- tions,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Object detection using sim2real domain randomization for robotic applica- tions,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.579588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c97248e-4f19-403e-a272-e51f21e8cccd · outbound

This paper cites Towards fully-synthetic training for industrial applications,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Towards fully-synthetic training for industrial applications,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.927638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4bbf88d1-be53-4c2f-a7a1-d3d82e9e3bac · outbound

This paper cites Training deep networks with synthetic data: Bridging the reality gap by domain randomization,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Training deep networks with synthetic data: Bridging the reality gap by domain randomization,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.916240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 36b4bb33-a692-4dc4-9c02-995ac821c0c5 · outbound

This paper cites Structured domain random- ization: Bridging the reality gap by context-aware synthetic data,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Structured domain random- ization: Bridging the reality gap by context-aware synthetic data,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.905571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7f4d99b1-fbdd-46e2-9501-ac3f6a064234 · outbound

This paper cites Efficientdet: Scalable and effi- cient object detection,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Efficientdet: Scalable and effi- cient object detection,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.895086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 74dbc006-f900-47c4-b2f2-71e96d7dd554 · outbound

This paper cites Ultralytics YOLO,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Ultralytics YOLO,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.598515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45aa80e7-a1de-4e5d-b383-66338cb61da9 · outbound

This paper cites End-to-end object detection with transformers,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study End-to-end object detection with transformers,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.878083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c1a4a256-72a9-4528-85f1-09a239405f45 · outbound

This paper cites A review on anchor assignment and sampling heuristics in deep learning-based object detection,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study A review on anchor assignment and sampling heuristics in deep learning-based object detection,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.868015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f68b0670-228d-47bb-9e39-f6ebe4115513 · outbound

This paper cites A survey of modern deep learning based object detection models,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study A survey of modern deep learning based object detection models,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.857885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6cf669bb-2640-4dc3-a4c6-f2127945522a · outbound

This paper cites Mujoco: A physics engine for model-based control,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Mujoco: A physics engine for model-based control,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.848053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b8da3226-f3b2-4f03-8b37-13486bdf88e3 · outbound

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

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.617812Z

Source-reported events for the cited work

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Observation 154a4a2d-f4d0-4e3e-9df8-3597362aed46 · outbound

This paper cites Shreiner, G.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Shreiner, G

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.838669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ca0e98d5-070b-4ebb-8611-b1acb93cb0a0 · outbound

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

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Faster r-cnn: Towards real- time object detection with region proposal networks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.828238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1f1c8001-bc3d-4a84-9f66-01216784e742 · outbound

This paper cites Blender - a 3d modelling and rendering package,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Blender - a 3d modelling and rendering package,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.817928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a374e048-d228-43bc-9bde-9850567e2287 · outbound

This paper cites an unresolved cited work.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:35:20.807047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e8ec2d5a-2b24-40a7-9aef-4dcc79322940 · outbound

This paper cites Pybullet, a python module for physics simulation for games, robotics and machine learning,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Pybullet, a python module for physics simulation for games, robotics and machine learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.796285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f86614c4-682d-46be-a9a3-89fd1593856e · outbound

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

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:20.640258Z

Source-reported events for the cited work

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Observation cc4bc76c-0770-462c-b4b6-45f6ce290fe7 · outbound

This paper cites Framing image descrip- tion as a ranking task: Data, models and evaluation metrics,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Framing image descrip- tion as a ranking task: Data, models and evaluation metrics,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.785418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7326ece9-a3cd-4728-a55e-4841627ff12f · outbound

This paper cites CC Texture Dataset,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study CC Texture Dataset,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.774313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7415777b-7e52-484c-9406-a91dd4b8ef67 · outbound

This paper cites Bg-20k: A diverse background dataset for computer vision applications,.

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study Bg-20k: A diverse background dataset for computer vision applications,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.763316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 86038d24-5a81-48bb-a295-75845b1a52b1 · outbound

This paper cites [Online].

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study [Online]

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.752841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8a3ae62f-ddc0-485a-919a-5c3b8efe3256 · outbound

This paper cites [Online].

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study [Online]

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.741438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 841cea67-5ba8-4559-a52d-5e623311b4a0 · outbound

This paper cites [Online].

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study [Online]

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:20.730815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 45d49f08-292c-416e-8e70-718152752bc6 · inbound

The Power of Light: Improving Synthetic-to-Real Domain Adaptation through Physically-Based Indirect Illumination cites this paper.

The Power of Light: Improving Synthetic-to-Real Domain Adaptation through Physically-Based Indirect Illumination Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:39:42.339789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.