A Literature-based Performance Assessment of the YOLO (You Only Look Once) CNN Approach for Real-time Object Detection

Real-time object identification is considered as one of the major catalysts for computer vision, such as video surveillance, autonomous driving, robotics, and augmented reality. You Only Look Once (YOLO) is a state-of-the-art object detection algorithm based on Convolutional Neural Networks (CNNs) that provides an efficient solution by utilizing both classification and localization in a single forward pass through the network. This review provides a comprehensive overview of YOLO’s architecture, key innovations, comparable performance, challenges, and its impact on the field of real-time object detection. It also discusses the improvements that can be made in subsequent versions of YOLO and explores potential future research approaches.