YOLO models are used for object detection tasks, serving as efficient object detectors. They employ classifiers and regression techniques to directly create bounding boxes and class probabilities through activation functions and feature maps; they extract relevant information from input images. Optimization methods enhance their accuracy and speed, making them valuable in applications requiring real-time object detection, such as surveillance, autonomous driving, and robotics