• DOI:10.1109/CVPR.2016.91
Corpus ID: 206594738

You Only Look Once: Unified, Real-Time Object Detection

@article{Redmon2016YouOL,
  title={You Only Look Once: Unified, Real-Time Object Detection},
  author={Joseph Redmon and S. Divvala and Ross B. Girshick and Ali Farhadi},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={779-788}
}

Joseph Redmon, S. Divvala, +1 author Ali Farhadi

Published 2016

Computer Science

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance… CONTINUE READING

View on IEEE

arxiv.org

Save to Library

Create Alert

Cite

Launch Research Feed