A Deep Look into YOLOv5: Theoretical Foundations and Structural Analysis of an Object Detector

YOLOv5 (You Only Look Once version 5) is one of the fastest, most accurate, and efficient object detection models. This article presents a theoretical analysis of the YOLOv5 model architecture, training methods, and performance metrics. It delves into the main components of the model, i.e., backbone, neck, and head modules, and reviews the optimization strategies employed to boost detection performance. In addition, this study compares YOLOv5 with older versions of YOLO and other widely used object detection models, providing an understanding of the advantages and weaknesses of this model. The ultimate goal is to provide a better understanding of YOLOv5’s internal workings to researchers and practitioners working on computer vision.