Comparative Study of Deep Learning Algorithms Between YOLOv5, YOLOv7 and YOLOv8 As Fast and Robust Outdoor Object Detection Solutions

Abstract

object detection is one of the most popular applications among young people, especially among millennials and generation Z. The use of object detection has become widespread in various aspects of daily life, such as face recognition, traffic management, and autonomous vehicles. The use of object detection has expanded in various aspects of daily life, such as face recognition, traffic management, and autonomous vehicles. To perform object detection, large and complex datasets are required. Therefore, this research addresses what object detection algorithms are suitable for object detection. In this research, i will compare the performance of several algorithms that are popular among young people, such as YOLOv5, YOLOv7, and YOLOv8 models. By conducting several Experiment Results such as Detection Results, Distance Traveled Experiment Results, Confusion Matrix, and Experiment Results on Validation Dataset, I aim to provide insight into the advantages and disadvantages of these algorithms. This comparison will help young researchers choose the most suitable algorithm for their object detection task.