YOLO Model

YOLO (You Only Look Once) models are a family of real-time object detection algorithms used extensively in computer vision. Current research focuses on improving YOLO's accuracy and efficiency across diverse applications, including agricultural automation, search and rescue, industrial monitoring, and assistive technologies for the visually impaired, with variations like YOLOv5, YOLOv8, and YOLOv7 being prominent. These improvements involve optimizing model architectures (e.g., incorporating attention mechanisms, lightweight backbones), enhancing training techniques (e.g., data augmentation, quantization-aware training), and adapting the models for resource-constrained devices. The resulting advancements have significant implications for various fields, enabling faster and more accurate object detection in real-world scenarios.