Real-Time Object Detectors using YOLOv7 Algorithm: A Review

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Real-Time Object Detectors using YOLOv7 Algorithm: A Review

Ansh lodhi1,*, Chandan kumar2, Chetan chauhan3

1,2,3B.tech-CSE Student, IIMT College of Engg, Greater Noida, UP, India

*Corres. Author: yatendrakumar2512@gmail.com

Guide: Prof. Deepesh kumar

Assistant Professor, Department of CSE

Department Of Computer Science and Engineering

IIMT College Of Engineering, Greater Noida

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

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