YOLO编辑历史

# YOLO (You Only Look Once)

YOLO (You Only Look Once) is a state-of-the-art, real-time object detection system in computer vision. Unlike traditional two-stage detectors (e.g., Faster R-CNN) that first propose candidate regions and then classify them, YOLO frames detection as a single regression problem, directly predicting bounding boxes and class probabilities from full images in one evaluation. Its key innovation is combining speed and accuracy, achieving real-time processing (45–155 frames per second on standard GPUs) while maintaining competitive detection performance. First introduced by Joseph Redmon and colleagues in 2015, YOLO has undergone multiple revisions, becoming a foundational framework in autonomous driving, surveillance, and embedded vision.

## 1 History and Development

### 1.1 Original YOLO (2015)

The original YOLO was proposed by Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi in their 2015 paper "You Only Look Once: Unified, Real-Time Object Detection." It used a single convolutional neural network (CNN) that divided the input image into an S × S grid. Each grid cell predicted B bounding boxes and class probabilities for C object classes. The model achieved 45 FPS on a Titan X GPU, with 63.4% mean average precision (mAP) on the PASCAL VOC 2007 benchmark. Despite lower accuracy than contemporary two-stage detectors, its speed revolutionized real-time detection.

### 1.2 YOLOv2 (YOLO9000)

YOLOv2, also known as YOLO9000, was introduced by Redmon and Farhadi in 2016. It incorporated batch normalization, anchor boxes (pre-defined bounding box shapes), and a new backbone called Darknet-19. The most notable innovation was joint training on the COCO detection dataset and the ImageNet classification dataset, enabling the model to detect over 9,000 object categories. YOLOv2 achieved 78.6 mAP on VOC 2007 at 67 FPS and introduced the concept of dimension clusters for anchor box priors.

### 1.3 YOLOv3

Released in 2018, YOLOv3 improve