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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM61077
Volume:
10 |
4 |
April 2026
Pages:
Publication Frequency:
Monthly (12 issues per year)
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.
â 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.
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Publication Date
4/23/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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