Authors : Bhargavi Vijendra Sangam; Swetha V.; Ananya T.

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/evm6ju8t

Scribd : https://tinyurl.com/4v5spjtv

DOI : https://doi.org/10.38124/ijisrt/26jun198

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Object detection has become one of the most critical fields of study in computer vision due to its broad application range including surveillance, health care, autonomous driving, agriculture, robotics, and industrial automation. YOLO is considered one of the most efficient object detection frameworks due to its unique feature of being able to localize and classify objects in one feedforward network pass. Starting with its inception in 2016, YOLO architecture underwent multiple changesto improve accuracy, inference speed, and efficiency. This review paper provides an overview of how YOLO architectures have evolved from YOLO v1 to YOLO v8. The advancements, strengths, weaknesses, and enhancements of each iteration are discussed in detail. In addition, some of the applications of YOLO for object detection are analyzed. The paper concludes with potential future challenges and trends for real-time object detection.

Keywords : YOLO, Object Detection, Deep Learning, Computer Vision, Real-Time Detection, CNN.

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