Yolo-Based Fast and Accurate Object Detection for Real-Time Applications.
Keywords:
foundation, efficiency, highlightsAbstract
Real-time object detection is a critical task in computer vision with applications in autonomous vehicles, surveillance, robotics, and smart monitoring systems. Traditional object detection methods often struggle with balancing accuracy and speed, making them unsuitable for real-time scenarios. This study explores the YOLO (You Only Look Once) algorithm, a deep learning-based framework known for its high-speed and accurate object detection capabilities. The YOLO model processes entire images in a single neural network pass, enabling efficient multi-object detection with minimal latency. The proposed system enhances detection performance by utilizing optimized anchor boxes, improved feature extraction, and transfer learning techniques. Experimental results demonstrate that YOLO outperforms traditional detection methods in terms of detection speed, accuracy, and computational efficiency, making it ideal for real-time applications. This research highlights the significance of YOLO-based object detection in various industries and sets the foundation for future advancements in AI-driven real-time vision systems.
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