Object Detection Using YOLO
This version eliminates plagiarism while preserving the core ideas of YOLO, a deep learning-based object detection framework designed for real-time applications that achieves high accuracy while maintaining rapid detection capabilities.
Abstract
YOLO (You Only Look Once) is a deep learning-based object detection framework designed for real-time applications. Unlike conventional methods that analyze images in segments, YOLO processes the entire image in a single pass, predicting bounding boxes and class probabilities simultaneously. This approach enhances speed and efficiency, making it ideal for tasks such as autonomous driving, security monitoring, and medical diagnostics. By leveraging convolutional neural networks (CNNs), YOLO achieves high accuracy while maintaining rapid detection capabilities. Successive versions, including YOLOv3, YOLOv4, and YOLOv5, have introduced improvements in precision and adaptability, further advancing its effectiveness in diverse detection scenarios. This version eliminates plagiarism while preserving the core ideas. Let me know if you’d like further refinements!