Autonomous Underwater Robotic System for Aquaculture Applications

TL;DR

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.

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 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.

Figures (7)

• Figure 1: Aquaculture net pens inspection project concept. An ROV takes camera input, apply deep learning model, perform aqua-net defect detection, and gets reference input via feedback control law for tracking the net plane.
• Figure 2: Proposed block diagram of the system.
• Figure 3: ROV distance approximation and control.
• Figure 4: Aquaculture net pens defect dataset.
• Figure 5: The architectural diagram of YOLOv5 yolov5.