ODYSSEE: Oyster Detection Yielded by Sensor Systems on Edge Electronics

Oysters are a vital keystone species in coastal ecosystems, providing significant economic, environmental, and cultural benefits. As the importance of oysters grows, so does the relevance of autonomous systems for their detection and monitoring. However, current monitoring strategies often rely on destructive methods. While manual identification of oysters from video footage is non-destructive, it is time-consuming, requires expert input, and is further complicated by the challenges of the underwater environment.To address these challenges, we propose a novel pipeline using stable diffusion to augment a collected real dataset with realistic synthetic data. This method enhances the dataset used to train a YOLOv10-based vision model. The model is then deployed and tested on an edge platform in underwater robotics, achieving a state-of-the-art 0.657 mAP@50 for oyster detection on the Aqua2 platform.

@article{lin2025_2409.07003,
  title={ ODYSSEE: Oyster Detection Yielded by Sensor Systems on Edge Electronics },
  author={ Xiaomin Lin and Vivek Mange and Arjun Suresh and Bernhard Neuberger and Aadi Palnitkar and Brendan Campbell and Alan Williams and Kleio Baxevani and Jeremy Mallette and Alhim Vera and Markus Vincze and Ioannis Rekleitis and Herbert G. Tanner and Yiannis Aloimonos },
  journal={arXiv preprint arXiv:2409.07003},
  year={ 2025 }
}