YOLO (You Only Look Once) object detection works by dividing an image into a grid and predicting bounding boxes and class probabilities for objects within each grid cell. Utilizing a deep convolutional neural network (CNN), typically implemented in the Darknet framework, YOLO extracts features and predicts bounding boxes directly. It's efficient due to its single forward pass architecture, enabling real-time processing. Recent advancements like YOLOv4 and YOLOv5 have improved accuracy and speed. Unlike segmentation methods, YOLO does not rely on region proposals, resulting in faster inference. Metrics such as mAP (mean Average Precision) evaluate its performance.