A Real-Time Tiny Detection Model for Stem End and Blossom End of Navel Orange

@article{Sun2019ART,
  title={A Real-Time Tiny Detection Model for Stem End and Blossom End of Navel Orange},
  author={Xiaoye Sun and Shaoyun Xu and Gongyan Li},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.09994}
}
To distinguish the stem end and blossom end of navel orange from its black spot, we propose a real-time tiny detection model (RTTD) with low computational cost, compact architecture and high detection accuracy. In particular, based on the characteristics of the data, we apply pure dense connectivity to limit and simplify the design of the model architecture and use k-means clustering to set the size and aspect ratios of the default boxes. The architecture of model is based on deeply supervised… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 41 REFERENCES

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

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2015
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

DSOD: Learning Deeply Supervised Object Detectors from Scratch

  • 2017 IEEE International Conference on Computer Vision (ICCV)
  • 2017
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

Squeeze-and-Excitation Networks

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
  • 2017
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

YOLO9000: better

J. Redmon, A. Farhadi
  • faster, stronger. arXiv preprint
  • 2017
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Deep learning in agriculture: A survey

  • Computers and Electronics in Agriculture
  • 2018
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

YOLOv3: An Incremental Improvement

VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

L

G. Huang, Z. Liu
  • van der Maaten, K. Q. Weinberger and Ieee. Densely Connected Convolutional Networks. In 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp. 2261-2269
  • 2017
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

Similar Papers