Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

@article{Ren2015FasterRT,
  title={Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks},
  author={Shaoqing Ren and Kaiming He and Ross B. Girshick and Jian Sun},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2015},
  volume={39},
  pages={1137-1149}
}
  • Shaoqing Ren, Kaiming He, +1 author J. Sun
  • Published 2015
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. [...] Key Method An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection.Expand
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This paper proposes a unified deep neural network building upon the prominent Faster R-CNN framework that achieves superior performance to the state of the arts, especially for small scale objects on PASCAL object detection challenge dataset. Expand
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