Redefining Object Detection: Harnessing the Full Potential of YOLO

Abstract

Recently, there has been a notable use of deep learning methodologies, namely convolutional neural networks (CNNs), in computer vision, specifically about the significant matter of object recognition. The "You Only Look Once" (YOLO) technique is a strategy that offers a rapid and dependable approach for detecting objects in both static and dynamic visual content. This article presents a comprehensive overview of YOLO, including its historical context, architectural design, and performance evaluation on many widely accepted benchmarks within the industry. In addition, we identify the study's limitations and provide suggestions for further investigations. In conclusion, it is evident that the YOLO approach is highly effective in object recognition, surpassing its predecessors in performance.  

Keywords

Data Pre-Processing, Training, Evaluation