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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM61077

YOLOv8: A Comprehensive Review of Architecture, Advancements, and Applications in Real-Time Object Detection

Stream/Area:

-

Subject:

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Paper ID:

IJSREM61077

Volume:

10 |

4 |

April 2026

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem61077

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

KS

Kiran Sahu

BRS

Borra Rupa Sri

TKS

Trisha Kumari Sahu

GU

Govindu Usha

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Abstract

– Recent advancements in deep learning have significantly improved object detection systems, with the YOLO (You Only Look Once) family emerging as a dominant paradigm for real-time detection. YOLOv8, developed by Ultralytics, introduces architectural refinements such as anchor-free detection, decoupled heads, and multi-task capabilities. This paper presents a comprehensive review of YOLOv8, including its architecture, innovations, performance benchmarks, and applications. Furthermore, the study compares YOLOv8 with prior YOLO versions and other detection frameworks while highlighting limitations and future research directions.

KEYWORDS

object detection computer vision YOLOv8 YOLO deep learning decoupled head convolutional neural networks (CNNs) feature pyramid networks (FPN) image recognition edge computing

Cite This Article

Kiran Sahu, Borra Rupa Sri, Trisha Kumari Sahu, Govindu Usha, "YOLOv8: A Comprehensive Review of Architecture, Advancements, and Applications in Real-Time Object Detection," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, April 2026. https://doi.org/10.55041/ijsrem61077

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4/23/2026

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