A Comprehensive Review of YOLOv5: Advances in Real-Time Object Detection

Sandeep Kumar Jaiswal; Rohit Agrawal

doi:10.55524/ijircst.2024.12.3.12

Abstract

YOLOv5 represents a significant advancement in the field of real-time object detection, building upon the YOLO (You Only Look Once) series' legacy. This paper provides a comprehensive review of YOLOv5, examining its architecture, innovations, performance benchmarks, and applications. We also compare YOLOv5 with previous YOLO versions and other state-of-the-art object detection models, highlighting its strengths and limitations. Through this review, we aim to offer insights into the evolution of YOLOv5 and its impact on the field of computer vision.

Keywords

YOLOv5, YOLOv4, Object Detection, Real-time, Performance Evaluation

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Cites this article as

S. K. Jaiswal, R. Agrawal, "A Comprehensive Review of YOLOv5: Advances in Real-Time Object Detection", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), vol. 12, no. 3, pp. 75-80, 2024. doi: https://doi.org/10.55524/ijircst.2024.12.3.12

Corresponding Author

Sandeep Kumar Jaiswal

M. Tech Scholar, Department of Computer Science and Engineering, BN College of Engineering and Technology, Lucknow, India

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