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A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS
by
Córdova-Esparza, Diana-Margarita
, Terven, Juan
, Romero-González, Julio-Alejandro
in
Accuracy
/ Algorithms
/ Autonomous cars
/ Bibliometrics
/ Boxes
/ Computer vision
/ Datasets
/ deep learning
/ Detectors
/ Iterative methods
/ Localization
/ Machine vision
/ Neural networks
/ object detection
/ Object recognition
/ Real time
/ Robotics
/ Surveillance
/ YOLO
2023
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A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS
by
Córdova-Esparza, Diana-Margarita
, Terven, Juan
, Romero-González, Julio-Alejandro
in
Accuracy
/ Algorithms
/ Autonomous cars
/ Bibliometrics
/ Boxes
/ Computer vision
/ Datasets
/ deep learning
/ Detectors
/ Iterative methods
/ Localization
/ Machine vision
/ Neural networks
/ object detection
/ Object recognition
/ Real time
/ Robotics
/ Surveillance
/ YOLO
2023
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Do you wish to request the book?
A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS
by
Córdova-Esparza, Diana-Margarita
, Terven, Juan
, Romero-González, Julio-Alejandro
in
Accuracy
/ Algorithms
/ Autonomous cars
/ Bibliometrics
/ Boxes
/ Computer vision
/ Datasets
/ deep learning
/ Detectors
/ Iterative methods
/ Localization
/ Machine vision
/ Neural networks
/ object detection
/ Object recognition
/ Real time
/ Robotics
/ Surveillance
/ YOLO
2023
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A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS
Journal Article
A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS
2023
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Overview
YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO’s evolution, examining the innovations and contributions in each iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with transformers. We start by describing the standard metrics and postprocessing; then, we discuss the major changes in network architecture and training tricks for each model. Finally, we summarize the essential lessons from YOLO’s development and provide a perspective on its future, highlighting potential research directions to enhance real-time object detection systems.
Publisher
MDPI AG
Subject
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