Paper Citation Record · LEDGER

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis

As of 12 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2412.19467.

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

pith.paper-citation-record.v1
2412.19467 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:36:05.258294Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

20 of 20 outbound references displayed

verified exact1
verified fuzzy17
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 796d0d4a-885f-469e-9f5f-ec2b47748eea · outbound

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

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis You only look once: Unified, real -time object detection,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:36:05.738150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 503c9ab9-8e9f-450e-88cd-01e77adf3ba9 · outbound

This paper cites A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo -nas,.

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo -nas,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:36:05.721567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a09a13fb-d433-4565-8e92-ef3fc0047da9 · outbound

This paper cites Complexer-yolo: Real-time 3d object detection and tracking on semantic point clouds ,.

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis Complexer-yolo: Real-time 3d object detection and tracking on semantic point clouds ,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:36:05.705265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 99914ad9-9567-4047-a531-3e9a07867e3f · outbound

This paper cites Object detection and tracking with yolo and the sliding innovation filter,.

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis Object detection and tracking with yolo and the sliding innovation filter,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:36:05.689422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9d58c418-5f4e-469d-a753-8a56f8380616 · outbound

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

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis Faster r -cnn: Towards real-time object detection with region proposal networks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:36:05.672966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fbfe3251-418c-4ea8-8d35-097c6ffdaf37 · outbound

This paper cites Improvement of object detection based on faster r -cnn and yolo,.

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis Improvement of object detection based on faster r -cnn and yolo,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:36:05.656908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9cf8a801-641b-4028-b4d9-50d7bed9f448 · outbound

This paper cites SSD: Single shot multibox detector,.

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis SSD: Single shot multibox detector,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:36:05.639440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 91765eb0-6c11-419c-ab39-c90d09a05cc9 · outbound

This paper cites Focal loss for dense object detection,.

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis Focal loss for dense object detection,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:36:05.623987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 02d96819-30eb-4a94-bdfd-32d3e86da4a7 · outbound

This paper cites Real -time object detection using an en semble of one stage and two stage object detection models with dynamic fine -tuning using kullback-leibler divergence,.

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis Real -time object detection using an en semble of one stage and two stage object detection models with dynamic fine -tuning using kullback-leibler divergence,

Reference 9

Resolution: metadata mismatch; raw_fallback, observed 2026-08-11T00:36:05.452419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4bc48693-bfc2-4ed6-80ac-e061e4e802f5 · outbound

This paper cites Mask r -cnn,.

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis Mask r -cnn,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:36:05.607823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3769d857-5e04-4b31-9d84-67541e3a7c84 · outbound

This paper cites Bike helmet detection dataset,.

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis Bike helmet detection dataset,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:36:05.590995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 17aef6d9-09c1-4b38-8fa9-33883a6f8161 · outbound

This paper cites Training object detection and recognition cnn models using data augmentation,.

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis Training object detection and recognition cnn models using data augmentation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:36:05.575530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0fa3396b-2db8-4e88-8897-34234142748c · outbound

This paper cites Dmac-yolo: A high-precision yolo v5s object detection model with a novel optimizer,.

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis Dmac-yolo: A high-precision yolo v5s object detection model with a novel optimizer,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:36:05.559591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8e51554a-4c71-4416-8683-7028c035372b · outbound

This paper cites Adam optimizer based deep learning approach for improving efficiency in license plate recognition,.

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis Adam optimizer based deep learning approach for improving efficiency in license plate recognition,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:36:05.542481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0d631124-8264-4664-9b95-6c0ca556bd08 · outbound

This paper cites Yolo -firi: Improved yolov5 for infrared image object detection,.

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis Yolo -firi: Improved yolov5 for infrared image object detection,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:36:05.520252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c586c7d5-30e5-4bd3-9d0e-32460b21c94f · outbound

This paper cites Sod -yolov8—enhancing yolov8 for small object detection in aerial imagery and traffic scenes,.

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis Sod -yolov8—enhancing yolov8 for small object detection in aerial imagery and traffic scenes,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:36:05.503027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4872808b-f671-442c-81a8-4e8b1f25c1fd · outbound

This paper cites What is YOLOv9: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector.

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis What is YOLOv9: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-11T00:36:05.326547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9c0bd7b3-8871-44b5-8b49-65e821c85685 · outbound

This paper cites YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems.

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T00:36:05.247116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38307f1f-b52f-40aa-af6b-358943bdccbf · outbound

This paper cites Efficient -lightweight yolo: Improving small object detection in yolo for aerial images,.

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis Efficient -lightweight yolo: Improving small object detection in yolo for aerial images,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:36:05.486813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 05bec2cb-d9bb-44ff-8e13-29f38d844a39 · outbound

This paper cites Vishaal C is a final -year undergraduate student in Computer Science and Engineering at the College of Engineering, Guindy, Chennai.

Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis Vishaal C is a final -year undergraduate student in Computer Science and Engineering at the College of Engineering, Guindy, Chennai

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:36:05.469346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.