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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM34437

Real-Time Object Detectors using YOLOv7 Algorithm: A Review

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM34437

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM34437

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

Al

Ansh lodhi

Ck

Chandan kumar

Cc

Chetan chauhan

PDk

Prof. Deepesh kumar

PDk

Prof. Deepesh kumar

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Abstract

YOLOv7, the latest iteration of the You Only Look Once (YOLO) algorithm, stands out in the field of real-time object detection by achieving a remarkable balance between speed and accuracy. This review highlights the primary features of YOLOv7, emphasizing its high accuracy, real-time performance, and efficiency. YOLOv7 surpasses previous YOLO versions and other popular detectors like Cascade Mask R-CNN. It achieves over 30 FPS on a GPU V100, making it suitable for applications requiring rapid and accurate object detection.

KEYWORDS

Real-time object detection High accuracy Fast inference speed Efficiency State-of-the-art

Cite This Article

Ansh lodhi, Chandan kumar, Chetan chauhan, Prof. Deepesh kumar, Prof. Deepesh kumar, "Real-Time Object Detectors using YOLOv7 Algorithm: A Review," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024. https://doi.org/10.55041/IJSREM34437

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12/31/45432

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