ODYSSEE: Oyster Detection Yielded by Sensor Systems on Edge Electronics
This work addresses the need for efficient, non-destructive monitoring of oysters in coastal ecosystems, which is important for environmental and economic stakeholders, though it is incremental as it builds on existing methods like YOLOv10 and stable diffusion.
The paper tackled the problem of non-destructive oyster detection in underwater environments by proposing a pipeline that uses stable diffusion for synthetic data augmentation and YOLOv10-based vision models, achieving a state-of-the-art 0.657 mAP@50 on the Aqua2 platform.
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.