A Comparative Study of YOLOv5 and YOLOv7 Object Detection Algorithms
Oluwaseyi Ezekiel Olorunshola, Martins E. Irhebhude, Abraham E. Evwiekpaefe
Abstract
This paper presents a comparative analysis of the widely accepted YOLOv5 and the latest version of YOLO which is YOLOv7. Experiments were carried out by training a custom model with both YOLOv5 and YOLOv7 independently in order to consider which one of the two performs better in terms of precision, recall, [email protected] and [email protected]:0.95. The dataset used in the experiment is a custom dataset for Remote Weapon Station which consists of 9,779 images containing 21,561 annotations of four classes gotten from Google Open Images Dataset, Roboflow Public Dataset and locally sourced dataset. The four classes are Persons, Handguns, Rifles and Knives. The experimental results of YOLOv7 were precision score of 52.8%, recall value of 56.4%, [email protected] of 51.5% and [email protected]:0.95 of 31.5% while that of YOLOv5 were precision score of 62.6%, recall value of 53.4%, [email protected] of 55.3% and [email protected]:0.95 of 34.2%. It was observed from the experiment conducted that YOLOv5 gave a better result than YOLOv7 in terms of precision, [email protected] and [email protected]:0.95 overall while YOLOv7 has a higher recall value during testing than YOLOv5. YOLOv5 records 4.0% increase in accuracy compared to YOLOv7.