Detecting and Counting Oysters

TL;DR

This paper presents a robotic and AI-based system using ROVs and Mask R-CNN to detect, count, and monitor oysters in Chesapeake Bay, aiding restoration efforts and water quality improvement.

Contribution

It introduces a novel approach combining underwater videography, database construction, and deep learning for oyster detection and counting.

Findings

Successful deployment of Mask R-CNN for oyster detection

Effective tracking of oysters in underwater videos

Potential for improved oyster restoration monitoring

Abstract

Oysters are an essential species in the Chesapeake Bay living ecosystem. Oysters are filter feeders and considered the vacuum cleaners of the Chesapeake Bay that can considerably improve the Bay's water quality. Many oyster restoration programs have been initiated in the past decades and continued to date. Advancements in robotics and artificial intelligence have opened new opportunities for aquaculture. Drone-like ROVs with high maneuverability are getting more affordable and, if equipped with proper sensory devices, can monitor the oysters. This work presents our efforts for videography of the Chesapeake bay bottom using an ROV, constructing a database of oysters, implementing Mask R-CNN for detecting oysters, and counting their number in a video by tracking them.