A Study of YOLO (You Only Look Once) to YOLOv8

摘要

YOLO, which stands for “You Only Look Once,” is an object detection algorithm that revolutionized real-time computer vision tasks by enabling fast and accurate object detection in images or videos. Traditional object detection algorithms involve multiple stages and are computationally expensive. but the YOLO is, on the other hand, approaches object detection as an issue with regression, predicting class and bounding box probabilities in a single pass straight from the unprocessed image pixels. , To forecast bounding boxes, the YOLO algorithm divides the input image into a grid. Objectness scores and class probabilities for objects present within every grid cell. These grid-based approaches allow YOLO to detect multiple objects of different classes in a single forward pass. By predicting bounding boxes and class probabilities together, YOLO achieves real-time processing speeds, making it highly suitable for applications such as autonomous driving, surveillance, and robotics. YOLO is a groundbreaking object detection algorithm that employs a grid-based approach to predict bounding boxes and class probabilities directly from input images, enabling real-time and efficient object detection for a wide range of applications.