Object Detection Using YOLOv5: A Deep Learning Approach

Object detection is a fundamental task in computer vision that involves identifying and localizing objects within images or video frames. This research focuses on implementing and evaluating the YOLOv5 (You Only Look Once version 5) model for real-time object detection. YOLOv5 is known for its efficiency, accuracy, and speed, making it a preferred choice for various applications such as autonomous driving, surveillance, and medical imaging. In this study, we explore the architecture, training process, and performance evaluation of YOLOv5 on benchmark datasets. The results demonstrate that YOLOv5 achieves high precision and recall, outperforming traditional object detection methods in terms of speed and accuracy. Keywords: Object Detection, YOLOv5, Deep Learning, Computer Vision, Real-Time Processing, Neural Networks.