用合成数据提升边缘设备上牡蛎检测精度
ODYSSEE: Oyster Detection Yielded by Sensor Systems on Edge Electronics
- 用稳定扩散生成逼真合成数据，增强真实数据集
- 在水下机器人平台实现0.657 mAP@50的检测性能
- 适合做水下生物监测与边缘智能部署的研究者
牡蛎是沿海生态系统的关键物种，具有重要的经济、环境和文化价值。随着牡蛎重要性提升，其自主检测与监测系统的需求日益增长。然而，现有监测方法多依赖破坏性手段。虽可从视频中人工识别牡蛎，但耗时长、需专家参与，且受水下环境影响大。为此，我们提出一种新流程：利用稳定扩散生成逼真合成数据，扩充真实采集数据集，并用于训练基于YOLOv10的视觉模型。该模型部署于边缘平台，在Aqua2水下机器人平台上测试，实现0.657 mAP@50的领先检测性能。
原文摘要 · Abstract (English)
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.
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