Autonomous Underwater Robotic System for Aquaculture Applications
Waseem Akram Muhayyuddin Ahmed Lyes Saad Saoud Lakmal Seneviratne Irfan Hussain
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 a clear
sequence of net images and inspect the status of the net via performing the
inspection tasks. This work contributes to the area of aquaculture inspection,
marine robotics, and deep learning aiming to reduce cost, improve quality, and
ease of operation.