ODYSSEE: エッジ電子機器上のセンサシステムによるカキ検出
ODYSSEE: Oyster Detection Yielded by Sensor Systems on Edge Electronics
Stable Diffusionで実データを水増しし、YOLOv10ベースのモデルを水中ロボットのエッジ環境で動かしてカキを検出、mAP@50 0.657を達成した研究。
著者: Xiaomin Lin, Vivek Mange, Arjun Suresh, Bernhard Neuberger, Aadi Palnitkar, Brendan Campbell, Alan Williams, Kleio Baxevani, Jeremy Mallette, Alhim Vera, Markus Vincze, Ioannis Rekleitis, Herbert G. Tanner, Yiannis Aloimonos
分類: cs.CV, cs.RO
原文アブストラクト
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.
