# OysterNet: Enhanced Oyster Detection Using Simulation

arXiv: https://arxiv.org/abs/2209.08176 · HTML: https://mlsanity.com/paper/2209.08176/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images](https://mlsanity.com/paper/1702.07392/) (2017)
- [Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice](https://mlsanity.com/paper/1911.01685/) (2019)
- [UWGAN: Underwater GAN for Real-world Underwater Color Restoration and Dehazing](https://mlsanity.com/paper/1912.10269/) (2019)
- [EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following](https://mlsanity.com/paper/2106.15045/) (2021)
- [DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization](https://mlsanity.com/paper/2003.05523/) (2020)
- [Detecting and Counting Oysters](https://mlsanity.com/paper/2105.09758/) (2021)
- [Meander Based River Coverage by an Autonomous Surface Vehicle](https://mlsanity.com/paper/1908.02813/) (2019)
- [Autonomous Marine Sampling Enhanced by Strategically Deployed Drifters in Marine Flow Fields](https://mlsanity.com/paper/1811.10103/) (2018)

## Built on by (most cited first, 11 of 11)

- [SeaDroneSim: Simulation of Aerial Images for Detection of Objects Above Water](https://mlsanity.com/paper/2210.16107/) (2022)
- [MarineDet: Towards Open-Marine Object Detection](https://mlsanity.com/paper/2310.01931/) (2023)
- [ODYSSEE: Oyster Detection Yielded by Sensor Systems on Edge Electronics](https://mlsanity.com/paper/2409.07003/) (2024)
- [Whale Detection Enhancement through Synthetic Satellite Images](https://mlsanity.com/paper/2308.07766/) (2023)
- [MARVIS: Motion & Geometry Aware Real and Virtual Image Segmentation](https://mlsanity.com/paper/2403.09850/) (2024)
- [Detecting Olives with Synthetic or Real Data? Olive the Above](https://mlsanity.com/paper/2308.08271/) (2023)
- [DREAM: Domain-aware Reasoning for Efficient Autonomous Underwater Monitoring](https://mlsanity.com/paper/2509.13666/) (2025)
- [Detecting Endangered Marine Species in Autonomous Underwater Vehicle Imagery Using Point Annotations and Few-Shot Learning](https://mlsanity.com/paper/2406.01932/) (2024)
- [RAFT -- A Domain Adaptation Framework for RGB & LiDAR Semantic Segmentation](https://mlsanity.com/paper/2505.04529/) (2025)
- [Synthetic Data Augmentation for Enhanced Chicken Carcass Instance Segmentation](https://mlsanity.com/paper/2507.18558/) (2025)
- [One Model, Two Worlds: Bidirectional Sonar-Optical Translation](https://mlsanity.com/paper/2609.06253/) (2026)

## Abstract (arXiv)

Oysters play a pivotal role in the bay living ecosystem and are considered the living filters for the ocean. In recent years, oyster reefs have undergone major devastation caused by commercial over-harvesting, requiring preservation to maintain ecological balance. The foundation of this preservation is to estimate the oyster density which requires accurate oyster detection. However, systems for accurate oyster detection require large datasets obtaining which is an expensive and labor-intensive task in underwater environments. To this end, we present a novel method to mathematically model oysters and render images of oysters in simulation to boost the detection performance with minimal real data. Utilizing our synthetic data along with real data for oyster detection, we obtain up to 35.1% boost in performance as compared to using only real data with our OysterNet network. We also improve the state-of-the-art by 12.7%. This shows that using underlying geometrical properties of objects can help to enhance recognition task accuracy on limited datasets successfully and we hope more researchers adopt such a strategy for hard-to-obtain datasets.