YOLO Algorithm for Real-Time Object Detection

Abstract

This paper provides a comprehensive analysis of YOLO (You Only Look Once), a real-time object detection algorithm that has revolutionized the field of computer vision. Developed by Joseph Redmon and colleagues, YOLO's innovative architecture applies a single convolutional neural network to the entire image, enabling simultaneous prediction of multiple bounding boxes and class probabilities. The paper covers the evolution of YOLO from its initial version to the latest iteration, detailing key improvements in speed and accuracy. It also explores YOLO's applications across various domains, including autonomous vehicles, surveillance, medical imaging, robotics, and retail inventory management. The paper discusses the challenges YOLO faces, such as detecting small objects and handling occlusion, and outlines future research directions to enhance its robustness and adaptability. By highlighting YOLO's significant contributions and ongoing advancements, this paper underscores its pivotal role in advancing real-time object detection technology.