Autonomous Underwater Robotic System for Aquaculture Applications
Waseem Akram, Muhayyuddin Ahmed, Lakmal Seneviratne, and Irfan Hussain

TL;DR
This paper presents an autonomous underwater robotic system utilizing deep learning for real-time detection of aquaculture net defects, aiming to enhance inspection efficiency and reduce operational costs.
Contribution
It introduces a novel integrated system combining deep learning and feedback control for autonomous underwater net inspection in aquaculture.
Findings
Effective detection of biofouling, vegetation, and net holes.
Real-time inspection capability demonstrated.
Reduced need for human divers and ROVs.
Abstract
Aquaculture is a thriving food-producing sector producing over half of the global fish consumption. However, these aquafarms pose significant challenges such as biofouling, vegetation, and holes within their net pens and have a profound effect on the efficiency and sustainability of fish production. Currently, divers and/or remotely operated vehicles are deployed for inspecting and maintaining aquafarms; this approach is expensive and requires highly skilled human operators. This work aims to develop a robotic-based automatic net defect detection system for aquaculture net pens oriented to on- ROV processing and real-time detection of different aqua-net defects such as biofouling, vegetation, net holes, and plastic. The proposed system integrates both deep learning-based methods for aqua-net defect detection and feedback control law for the vehicle movement around the aqua-net to obtain…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsWater Quality Monitoring Technologies · Marine Bivalve and Aquaculture Studies · Microplastics and Plastic Pollution
