Image detection using YOLO-based object detection models

Abstract

Object detection is a core aspect of computer vision, enabling precise identification and localization of objects in images and videos. YOLO (You Only Look Once) revolutionized the field by framing object detection as a regression problem, using a single convolutional neural network for real-time detection. Combining speed, accuracy, and simplicity, YOLO has significantly impacted applications like autonomous driving, surveillance, and medical imaging. This journal reviews YOLO's architecture, evolution, applications, and challenges, highlighting its contributions to artificial intelligence and computer vision.