YOLOv4: A Breakthrough in Real-Time Object Detection | Paper Citation Record

Observation 853fc2af-9a14-47cf-b98e-8a99014442e0 · outbound

This paper cites A layer-wise surface deformation defect detection by convolutional neural networks in laser powder-bed fusion images.

YOLOv4: A Breakthrough in Real-Time Object Detection A layer-wise surface deformation defect detection by convolutional neural networks in laser powder-bed fusion images

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:55.092230Z

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Observation bce0aaa4-97a5-488b-b3d8-e6e9c5275394 · outbound

This paper cites Custom lightweight convolutional neural network architecture for automated detection of damaged pallet racking in warehousing & distribution centers.

YOLOv4: A Breakthrough in Real-Time Object Detection Custom lightweight convolutional neural network architecture for automated detection of damaged pallet racking in warehousing & distribution centers

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:55.077516Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 96324dfc-8418-4a85-bbbb-bc1409657256 · outbound

This paper cites Lightweight convolutional network for automated photovoltaic defect detection.

YOLOv4: A Breakthrough in Real-Time Object Detection Lightweight convolutional network for automated photovoltaic defect detection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:55.062866Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ee432ba7-81b5-48cb-b0f3-60d96f930c32 · outbound

This paper cites Comparative study of computational time that hog-based features used for vehicle detection.

YOLOv4: A Breakthrough in Real-Time Object Detection Comparative study of computational time that hog-based features used for vehicle detection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:55.046595Z

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Observation 4d918f76-0d66-4868-b430-fec2a4beec56 · outbound

This paper cites On combining classifiers.

YOLOv4: A Breakthrough in Real-Time Object Detection On combining classifiers

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:55.030560Z

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Observation 6f7804fd-4b14-45d6-a5ac-f5cc72431413 · outbound

This paper cites Action recognition by dense trajectories.

YOLOv4: A Breakthrough in Real-Time Object Detection Action recognition by dense trajectories

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:55.015657Z

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Observation 6bb59c21-43c3-43e0-be03-e3411bcfe44c · outbound

This paper cites A database for fine grained activity detection of cooking activities.

YOLOv4: A Breakthrough in Real-Time Object Detection A database for fine grained activity detection of cooking activities

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:55.001382Z

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Observation 793c9570-8359-4f53-b07d-26f33054feab · outbound

This paper cites Feature mapping for rice leaf defect detection based on a custom convolutional architecture.

YOLOv4: A Breakthrough in Real-Time Object Detection Feature mapping for rice leaf defect detection based on a custom convolutional architecture

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.987391Z

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Observation af1762f1-551b-4c9d-9e3e-b498634329fe · outbound

This paper cites Gun and knife detection based on faster r-cnn for video surveillance.

YOLOv4: A Breakthrough in Real-Time Object Detection Gun and knife detection based on faster r-cnn for video surveillance

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.973873Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 06976088-b15a-4b29-a88f-72ec3b870ad0 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

YOLOv4: A Breakthrough in Real-Time Object Detection Imagenet classification with deep convolutional neural networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-08T23:20:54.017148Z

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Observation 7a22adf9-0d13-4927-b73a-0f19d9c8a7bf · outbound

This paper cites Stable and compact design of memristive googlenet neural network.

YOLOv4: A Breakthrough in Real-Time Object Detection Stable and compact design of memristive googlenet neural network

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.951728Z

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Observation 235ea515-7379-42fa-88fd-126e37b0ba37 · outbound

This paper cites Brain tumor detection using mask r-cnn.

YOLOv4: A Breakthrough in Real-Time Object Detection Brain tumor detection using mask r-cnn

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.938222Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cbc208a5-2083-4de3-91db-507f5b6de0a0 · outbound

This paper cites Deep residual learning for image recognition.

YOLOv4: A Breakthrough in Real-Time Object Detection Deep residual learning for image recognition

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T23:20:54.030763Z

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Observation 14ece323-b1cb-4958-8891-822d832b405a · outbound

This paper cites Pedestrian detection based on faster r-cnn.

YOLOv4: A Breakthrough in Real-Time Object Detection Pedestrian detection based on faster r-cnn

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.915342Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a0114d27-98e7-4e88-9bd1-819a38c3443c · outbound

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

YOLOv4: A Breakthrough in Real-Time Object Detection Rich feature hierarchies for accurate object detection and semantic segmentation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T23:20:54.039486Z

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Observation e6cdfab6-e7d9-4397-a5a7-589fb42a03af · outbound

This paper cites Classification of picture art style based on vggnet.

YOLOv4: A Breakthrough in Real-Time Object Detection Classification of picture art style based on vggnet

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.892691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ad34c7cd-60a7-4c0a-a2c9-185475043755 · outbound

This paper cites A dynamic multi-mobile agent itinerary planning approach in wireless sensor networks via intuitionistic fuzzy set.

YOLOv4: A Breakthrough in Real-Time Object Detection A dynamic multi-mobile agent itinerary planning approach in wireless sensor networks via intuitionistic fuzzy set

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.878511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bb817217-1b77-4ef7-9188-35622ec0afe8 · outbound

This paper cites Statistical analysis and development of an ensemble- based machine learning model for photovoltaic fault detection.

YOLOv4: A Breakthrough in Real-Time Object Detection Statistical analysis and development of an ensemble- based machine learning model for photovoltaic fault detection

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.864542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2daf0359-a8f4-42ca-aa0f-b9ccccd59973 · outbound

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

YOLOv4: A Breakthrough in Real-Time Object Detection You only look once: Unified, real-time object detection

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-08T23:20:54.056645Z

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Observation 68f611c9-bd71-4429-bff2-41322e626dbd · outbound

This paper cites A gradient guided architecture coupled with filter fused representations for micro-crack detection in photovoltaic cell surfaces.

YOLOv4: A Breakthrough in Real-Time Object Detection A gradient guided architecture coupled with filter fused representations for micro-crack detection in photovoltaic cell surfaces

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.841737Z

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Observation 0dfbd45e-f827-453f-be21-e719e2fc0836 · outbound

This paper cites Yolo9000: better, faster, stronger.

YOLOv4: A Breakthrough in Real-Time Object Detection Yolo9000: better, faster, stronger

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-08T23:20:54.065749Z

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Observation 09ab5059-8367-4283-aab8-358105a06c72 · outbound

This paper cites YOLOv3: An Incremental Improvement.

YOLOv4: A Breakthrough in Real-Time Object Detection YOLOv3: An Incremental Improvement

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-08T23:20:54.069870Z

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Observation c9291299-a5a3-438f-b5ec-500f99917350 · outbound

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

YOLOv4: A Breakthrough in Real-Time Object Detection YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-08T23:20:54.074728Z

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Observation d113d2bb-beda-4167-8c34-ef01e3fd9f5e · outbound

This paper cites Comprehensive guide to ultralytics yolov5, 2023.

YOLOv4: A Breakthrough in Real-Time Object Detection Comprehensive guide to ultralytics yolov5, 2023

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.818337Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 757be2eb-00f2-4a4b-b15e-d2d3847f4829 · outbound

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

YOLOv4: A Breakthrough in Real-Time Object Detection YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T23:20:54.083320Z

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Observation 2e4f8284-e097-41c6-8b6b-7511123b520c · outbound

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

YOLOv4: A Breakthrough in Real-Time Object Detection Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-08T23:20:54.087826Z

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Observation be65c656-9d3d-4a0a-ac60-fa14a66ba84c · outbound

This paper cites Yolo: A brief history, 2023.

YOLOv4: A Breakthrough in Real-Time Object Detection Yolo: A brief history, 2023

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.795589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 485362be-592a-4a71-8b62-b774f7215150 · outbound

This paper cites Isolated bangla handwritten character recognition with convolutional neural network.

YOLOv4: A Breakthrough in Real-Time Object Detection Isolated bangla handwritten character recognition with convolutional neural network

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.782309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 25a4bd90-6468-48b8-92a4-5f8442070251 · outbound

This paper cites Yolo object detection explained, 2024.

YOLOv4: A Breakthrough in Real-Time Object Detection Yolo object detection explained, 2024

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.768419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7525ffb3-d03d-4f8f-9232-06fe66ff0995 · outbound

This paper cites Using deep convolutional neural network architectures for object classification and detection within x-ray baggage security imagery.

YOLOv4: A Breakthrough in Real-Time Object Detection Using deep convolutional neural network architectures for object classification and detection within x-ray baggage security imagery

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.754212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fed1836f-7e24-4337-9837-d4dce09e14a0 · outbound

This paper cites A frame-work assisting the visually impaired people: common object detection and pose estimation in surrounding environment.

YOLOv4: A Breakthrough in Real-Time Object Detection A frame-work assisting the visually impaired people: common object detection and pose estimation in surrounding environment

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.740628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f2e406c1-a1e0-4130-934a-fde840a836c7 · outbound

This paper cites An evaluation of yolo-based algorithms for hand detection in the kitchen.

YOLOv4: A Breakthrough in Real-Time Object Detection An evaluation of yolo-based algorithms for hand detection in the kitchen

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.726816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a6aef855-0e08-4659-b76a-fc2e7650f734 · outbound

This paper cites Improved yolov4 algorithm for safety management of on-site power system work.

YOLOv4: A Breakthrough in Real-Time Object Detection Improved yolov4 algorithm for safety management of on-site power system work

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.713097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 121dcd74-a5eb-4846-81f5-ca32a9a94a07 · outbound

This paper cites You only learn one representation: Unified network for multiple tasks.

YOLOv4: A Breakthrough in Real-Time Object Detection You only learn one representation: Unified network for multiple tasks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.699305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7706d2ad-6a4b-4448-9e96-77da877a12da · outbound

This paper cites Complete and accurate holly fruits counting using yolox object detection.

YOLOv4: A Breakthrough in Real-Time Object Detection Complete and accurate holly fruits counting using yolox object detection

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.685732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5557f9c1-7484-4c8f-884b-3173793cc1d6 · outbound

This paper cites Real-time multiple object tracking for safe cooking activities.

YOLOv4: A Breakthrough in Real-Time Object Detection Real-time multiple object tracking for safe cooking activities

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.672170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9991dcc6-4c30-48ce-a9d8-82b0da34d7b0 · outbound

This paper cites Augmented reality based interactive cooking guide.

YOLOv4: A Breakthrough in Real-Time Object Detection Augmented reality based interactive cooking guide

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.658334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 44599195-3c29-44db-8f05-f8079e3c9dfb · outbound

This paper cites A Comparative Analysis of YOLOv5, YOLOv8, and YOLOv10 in Kitchen Safety.

YOLOv4: A Breakthrough in Real-Time Object Detection A Comparative Analysis of YOLOv5, YOLOv8, and YOLOv10 in Kitchen Safety

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-08T23:20:54.351329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a6ff7076-a849-4000-bbd8-8c5740fc526d · outbound

This paper cites Comparing YOLOv5 Variants for Vehicle Detection: A Performance Analysis.

YOLOv4: A Breakthrough in Real-Time Object Detection Comparing YOLOv5 Variants for Vehicle Detection: A Performance Analysis

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-08T23:20:54.330727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 97e301ff-c94d-4a83-98c1-76720692ed3b · outbound

This paper cites A yolov6-based improved fire detection approach for smart city environments.

YOLOv4: A Breakthrough in Real-Time Object Detection A yolov6-based improved fire detection approach for smart city environments

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.644263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 801874fe-a331-4a86-91ef-2ffe743afbab · outbound

This paper cites What is YOLOv6? A Deep Insight into the Object Detection Model.

YOLOv4: A Breakthrough in Real-Time Object Detection What is YOLOv6? A Deep Insight into the Object Detection Model

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-08T23:20:54.309094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6bf656a5-9783-4110-abd9-a966809fdd0a · outbound

This paper cites Detection of guns and knives images based on yolo v7.

YOLOv4: A Breakthrough in Real-Time Object Detection Detection of guns and knives images based on yolo v7

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.630106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 09f31f9b-2b99-470d-896e-38f4c1c18626 · outbound

This paper cites Performance of yolov7 in kitchen safety while handling knife.

YOLOv4: A Breakthrough in Real-Time Object Detection Performance of yolov7 in kitchen safety while handling knife

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.615936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 88238cc2-524c-43f6-ae7e-7e9d1289bede · outbound

This paper cites Real time object detection with data variation.

YOLOv4: A Breakthrough in Real-Time Object Detection Real time object detection with data variation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.601578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9459505a-9ed1-4d73-8c9f-68c19e27a068 · outbound

This paper cites Comparative analysis of yolov8 and yolov10 in vehicle detection: Performance metrics and model efficacy.Vehicles, 6(3):1364– 1382, 2024.

YOLOv4: A Breakthrough in Real-Time Object Detection Comparative analysis of yolov8 and yolov10 in vehicle detection: Performance metrics and model efficacy.Vehicles, 6(3):1364– 1382, 2024

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.586897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9e7048bc-62d2-40b0-ba9d-228e3121037d · outbound

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

YOLOv4: A Breakthrough in Real-Time Object Detection A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.572831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ed76fd74-5325-49a6-9684-4b3bd8be5f2e · outbound

This paper cites Yolov9: Learning what you want to learn using programmable gradient information.

YOLOv4: A Breakthrough in Real-Time Object Detection Yolov9: Learning what you want to learn using programmable gradient information

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.558281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c28383d0-50eb-4340-a723-351b3f1e868a · outbound

This paper cites YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain.

YOLOv4: A Breakthrough in Real-Time Object Detection YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-08T23:20:54.185822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7329d892-bf01-45f8-a77d-486e76aa261c · outbound

This paper cites Nikhileswara Rao.

YOLOv4: A Breakthrough in Real-Time Object Detection Nikhileswara Rao

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.544572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8bbad4e7-9d5e-440b-832a-c201e7ec107a · outbound

This paper cites Yolov4: A fast and efficient object detection model, 2024.

YOLOv4: A Breakthrough in Real-Time Object Detection Yolov4: A fast and efficient object detection model, 2024

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.530140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c99a0991-e269-48c2-b03a-4bf48923e1aa · outbound

This paper cites Scaled-yolov4: Scaling cross stage partial network.

YOLOv4: A Breakthrough in Real-Time Object Detection Scaled-yolov4: Scaling cross stage partial network

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.515978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cc2d07c4-9770-4aae-8771-4c119e320b35 · outbound

This paper cites What is yolov4? a detailed breakdown, 2024.

YOLOv4: A Breakthrough in Real-Time Object Detection What is yolov4? a detailed breakdown, 2024

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.501585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c1df6806-0824-4821-831e-7eabe3f6e4d6 · outbound

This paper cites Yolov4: High-speed and precise object detection, 2024.

YOLOv4: A Breakthrough in Real-Time Object Detection Yolov4: High-speed and precise object detection, 2024

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.484691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7c6c2e85-7d0b-46e2-a25f-d18064696511 · outbound

This paper cites Accelerating Object Detection with YOLOv4 for Real-Time Applications.

YOLOv4: A Breakthrough in Real-Time Object Detection Accelerating Object Detection with YOLOv4 for Real-Time Applications

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-08T23:20:54.273055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 983785d9-56a6-467d-9f97-22f63dee9d6a · outbound

This paper cites Yolov4 and darknet for pothole detection, 2022.

YOLOv4: A Breakthrough in Real-Time Object Detection Yolov4 and darknet for pothole detection, 2022

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.469368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8157a8cb-4c67-4858-aeec-13aa6e8f8af3 · outbound

This paper cites Deployment of ai-based rbf network for photovoltaics fault detection procedure.

YOLOv4: A Breakthrough in Real-Time Object Detection Deployment of ai-based rbf network for photovoltaics fault detection procedure

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.454166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ae1e6aab-8fbd-4780-b3b8-44aced01a3df · outbound

This paper cites Domain modelling for a lightweight convolutional network focused on automated exudate detection in retinal fundus images.

YOLOv4: A Breakthrough in Real-Time Object Detection Domain modelling for a lightweight convolutional network focused on automated exudate detection in retinal fundus images

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.439081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bb785d0a-8171-4073-9449-9c6077e1782c · outbound

This paper cites Child emotion recognition via custom lightweight cnn architecture.

YOLOv4: A Breakthrough in Real-Time Object Detection Child emotion recognition via custom lightweight cnn architecture

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:20:54.424148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3c31260b-4104-4c3b-b437-a01c8c18b7a5 · outbound

This paper cites You Only Learn One Representation: Unified Network for Multiple Tasks.

YOLOv4: A Breakthrough in Real-Time Object Detection You Only Learn One Representation: Unified Network for Multiple Tasks

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-08T23:20:54.125137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers