YOLOv5 Model

YOLOv5, and its subsequent iterations (YOLOv7, YOLOv8, YOLOv9, YOLOv10), are a family of real-time object detection models designed for speed and accuracy. Current research focuses on improving these models' performance through architectural innovations like enhanced feature extraction networks (e.g., GELAN, FasterNet), optimized loss functions (e.g., EIoU), and techniques to handle small or occluded objects. These advancements have significant implications for various applications, including autonomous driving, healthcare (e.g., fall detection, fracture detection), agriculture (e.g., fruit counting), and industrial automation, enabling faster and more accurate object detection in real-world scenarios.