ODYSSEE: Oyster Detection Yielded by Sensor Systems on Edge Electronics

2024-09-11

arXiv.org

AI Summary

Conventional oyster monitoring methods suffer from high invasiveness, labor-intensive manual video analysis, and severe degradation due to underwater imaging artifacts. Method: This work proposes a lightweight vision-based detection system optimized for edge deployment. It introduces the first Stable Diffusion–based synthetic data augmentation pipeline tailored for underwater oyster imagery; adopts and optimizes YOLOv10 for resource-constrained edge platforms (NVIDIA Jetson/Aqua2); and integrates a robust underwater optical image preprocessing algorithm. Results: The system achieves 0.657 mAP@50 on the Aqua2 platform—the highest reported accuracy for oyster detection—enabling real-time, non-invasive field monitoring. This study presents the first high-accuracy deployment of YOLOv10 on computationally limited underwater robotic platforms and empirically validates the efficacy of generative data augmentation for underwater biological detection.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Hardware-aware ML
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Detecting oysters autonomously in underwater environments
• Overcoming destructive and time-consuming manual monitoring methods
• Enhancing dataset quality with synthetic data for better detection

Innovation

• Stable diffusion generates synthetic oyster data
• YOLOv10 model trained on augmented dataset
• Deployed on edge platform for underwater detection