Machine learning
YOLO（You Only Look Once）
YOLO（You Only Look Once）是由Redmon、Divvala、Girshick和Farhadi于2016年CVPR上提出的一种单阶段、端到端的卷积目标检测器。它将目标检测重构为一个单一的回归问题——直接从图像中预测边界框坐标和类别概率，一次前向传播即可完成——从而实现了先前R-CNN等两阶段方法无法达到的实时检测速度。最初的论文催生了一个被广泛采用的后续版本系列（YOLOv2至v11），这些版本在应用目标检测基准测试中持续占据主导地位。
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来源
- Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788. DOI: 10.1109/CVPR.2016.91 ↗
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. ISBN: 978-0-262-03561-3
如何引用本页
ScholarGate. (2026, June 3). YOLO: You Only Look Once — Unified, Real-Time Object Detection. ScholarGate. https://scholargate.app/zh/deep-learning/yolo
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