ML Anthology

End-to-End Solution for Tenebrio Molitor Rearing Monitoring with Uncertainty Estimation and Domain Shift Detection

Pawel Majewski, Piotr Lampa, Robert Burduk, Jacek Reiner
CVPRW 2024 pp. 5498-5507
doi:10.1109/CVPRW63382.2024.00559 /cvprw/2024/majewski2024cvprw-endtoend/

Abstract

The large-scale rearing of edible insects, of which Tenebrio Molitor is a representative, requires monitoring using vision systems to control the process and to detect anomalies. Previously proposed solutions by researchers relied on multiple modules related to specific tasks (calculated coefficients) and specific types of models (instance segmentation, semantic segmentation). Long processing times and difficulties in maintaining and updating modules encourage the search for a more condensed solution as an end-to-end model. This paper proposed a modified YOLOv8 architecture extended with additional heads related to specific tasks. Heads were trained on problem-oriented small datasets, which significantly reduced the time spent on sample annotation. The proposed solution also included estimation of prediction uncertainty based on variation among predictions in model ensemble and detection of domain shift phenomenon. Quantitative results from the conducted experiments confirmed the potential of the developed solution.

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Cite

Text

Majewski et al. "End-to-End Solution for Tenebrio Molitor Rearing Monitoring with Uncertainty Estimation and Domain Shift Detection." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00559

Markdown

[Majewski et al. "End-to-End Solution for Tenebrio Molitor Rearing Monitoring with Uncertainty Estimation and Domain Shift Detection." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/majewski2024cvprw-endtoend/) doi:10.1109/CVPRW63382.2024.00559

BibTeX

@inproceedings{majewski2024cvprw-endtoend,
  title     = {{End-to-End Solution for Tenebrio Molitor Rearing Monitoring with Uncertainty Estimation and Domain Shift Detection}},
  author    = {Majewski, Pawel and Lampa, Piotr and Burduk, Robert and Reiner, Jacek},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2024},
  pages     = {5498-5507},
  doi       = {10.1109/CVPRW63382.2024.00559},
  url       = {https://mlanthology.org/cvprw/2024/majewski2024cvprw-endtoend/}
}