Evaluating Deep Learning Assisted Automated Aquaculture Net Pens
  Inspection Using ROV
Waseem Akram Muhayyuddin Ahmed Lakmal Seneviratne Irfan Hussain
In marine aquaculture, inspecting sea cages is an essential activity for
managing both the facilities' environmental impact and the quality of the fish
development process. Fish escape from fish farms into the open sea due to net
damage, which can result in significant financial losses and compromise the
nearby marine ecosystem. The traditional inspection system in use relies on
visual inspection by expert divers or ROVs, which is not only laborious,
time-consuming, and inaccurate but also largely dependent on the level of
knowledge of the operator and has a poor degree of verifiability. This article
presents a robotic-based automatic net defect detection system for aquaculture
net pens oriented to on-ROV processing and real-time detection. The proposed
system takes a video stream from an onboard camera of the ROV, employs a deep
learning detector, and segments the defective part of the image from the
background under different underwater conditions. The system was first tested
using a set of collected images for comparison with the state-of-the-art
approaches and then using the ROV inspection sequences to evaluate its
effectiveness in real-world scenarios. Results show that our approach presents
high levels of accuracy even for adverse scenarios and is adequate for
real-time processing on embedded platforms.