Academy & Industry Research Collaboration Center (AIRCC)

Volume 12, Number 16, September 2022

Yolov5, Yolo-x, Yolo-r, Yolov7 Performance Comparison: A Survey


Ismat Saira Gillani1, Muhammad Rizwan Munawar2, Muhammad Talha3, Salman Azhar4, Yousra Mashkoor5, Muhammad Sami uddin6 and Usama Zafar7, 1Columbus State University, USA, 2COMSAT University, Pakistan, 3GC University Faisalabad, Pakistan, 4Air University, Pakistan, 5NED University, Pakistan, 6National University of Technology (NUTECH) Islamabad, Pakistan, 7GC University Faisalabad, Pakistan


YOLOv7 algorithm have taken the object detection domain by the storm as its real-time object detection capabilities out ran all other previous algorithms both in accuracy and speed [1]. YOLOv7 advances the state of the art results in object detection by inferring more quickly and accurately than its contemporaries. In this paper, we are going to present our work of implementing this SOTA deep learning model on a soccer game play video to detect the players and football. As the result, it detected the players, football and their movement in real time. We also analyzed and compared the YOLOv7 results against its previous versions including YOLOv4, YOLOv5 and YOLO-R. The code is available at:


Deep Learning, Object detection, Image segmentation, Instance segmentation, Network Architecture.