ML Anthology

Deep Census: AUV-Based Scallop Population Monitoring

Christopher Rasmussen, Jiayi Zhao, Danielle Ferraro, Arthur Trembanis
ICCVW 2017 pp. 2865-2873
doi:10.1109/ICCVW.2017.338 /iccvw/2017/rasmussen2017iccvw-deep/

Abstract

We describe an integrated system for vision-based counting of wild scallops in order to measure population health, particularly pre- and post-dredging in fisheries areas. Sequential images collected by an autonomous underwater vehicle (AUV) are independently analyzed by a convolutional neural network based on the YOLOv2 architecture [18], which offers state-of-the-art object detection accuracy at real-time speeds. To augment the training dataset, a denoising auto-encoder network is used to automatically upgrade manually-annotated approximate object positions to full bounding boxes, increasing the detection network's performance. The system can act as a tool to improve or even replace an existing offline manual annotation workflow, and is fast enough to function "in the loop" for AUV control.

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Cite

Text

Rasmussen et al. "Deep Census: AUV-Based Scallop Population Monitoring." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.338

Markdown

[Rasmussen et al. "Deep Census: AUV-Based Scallop Population Monitoring." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/rasmussen2017iccvw-deep/) doi:10.1109/ICCVW.2017.338

BibTeX

@inproceedings{rasmussen2017iccvw-deep,
  title     = {{Deep Census: AUV-Based Scallop Population Monitoring}},
  author    = {Rasmussen, Christopher and Zhao, Jiayi and Ferraro, Danielle and Trembanis, Arthur},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2017},
  pages     = {2865-2873},
  doi       = {10.1109/ICCVW.2017.338},
  url       = {https://mlanthology.org/iccvw/2017/rasmussen2017iccvw-deep/}
}