PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection

@article{Kim2016PVANETDB,
  title={PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection},
  author={Kye-Hyeon Kim and Yeongjae Cheon and Sanghoon Hong and Byung-Seok Roh and Minje Park},
  journal={CoRR},
  year={2016},
  volume={abs/1608.08021}
}
This paper presents how we can achieve the state-of-the-art accuracy in multicategory object detection task while minimizing the computational cost by adapting and combining recent technical innovations. Following the common pipeline of “CNN feature extraction + region proposal + RoI classification”, we mainly redesign the feature extraction part, since region proposal part is not computationally expensive and classification part can be efficiently compressed with common techniques like… CONTINUE READING
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  • We obtained solid results on well-known object detection benchmarks: 81.8% mAP (mean average precision) on VOC2007 and 82.5% mAP on VOC2012 (2nd place), while taking only 750ms/image on Intel i7-6700K CPU with a single core and 46ms/image on NVIDIA Titan X GPU.
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Fast algorithms for convolutional neural networks

  • Andrew Lavin, Scott Gray
  • arXiv preprint arXiv:1509.09308,
  • 2015

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