ML Anthology

Detection in Agricultural Contexts: Are We Close to Human Level?

Omer Wosner, Guy Farjon, Faina Khoroshevsky, Lena Karol, Oshry Markovich, Daniel A. Koster, Aharon Bar-Hillel
ECCVW 2020 pp. 375-390
doi:10.1007/978-3-030-65414-6_26 /eccvw/2020/wosner2020eccvw-detection/

Abstract

We consider detection accuracy in agricultural contexts. Five challenging datasets were collected and benchmarked, with three recent networks tested. Based on an initial analysis showing the importance of image resolution, models were trained and tested with a multiple-resolution procedure. Detection results were compared to human performance, judged based on the consistency of multiple annotators. A quantitative analysis was made highlighting the role of object scale and occlusion as detection failure causes. Finally, novel detection accuracy metrics were suggested based on the needs of agriculture tasks, and used in detector performance evaluation.

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Cite

Text

Wosner et al. "Detection in Agricultural Contexts: Are We Close to Human Level?." European Conference on Computer Vision Workshops, 2020. doi:10.1007/978-3-030-65414-6_26

Markdown

[Wosner et al. "Detection in Agricultural Contexts: Are We Close to Human Level?." European Conference on Computer Vision Workshops, 2020.](https://mlanthology.org/eccvw/2020/wosner2020eccvw-detection/) doi:10.1007/978-3-030-65414-6_26

BibTeX

@inproceedings{wosner2020eccvw-detection,
  title     = {{Detection in Agricultural Contexts: Are We Close to Human Level?}},
  author    = {Wosner, Omer and Farjon, Guy and Khoroshevsky, Faina and Karol, Lena and Markovich, Oshry and Koster, Daniel A. and Bar-Hillel, Aharon},
  booktitle = {European Conference on Computer Vision Workshops},
  year      = {2020},
  pages     = {375-390},
  doi       = {10.1007/978-3-030-65414-6_26},
  url       = {https://mlanthology.org/eccvw/2020/wosner2020eccvw-detection/}
}