Category Archives: Aquaculture

AI-based net inspection tool

We are currently working on new machine vision based net inspection algorithms that combine both conventional and artificial intelligence (AI) based machine vision. The project is partially funded by FHF – Norwegian Seafood Research Fund and the tools developed with help the aquaculture industry reduce the risk of escaped salmon.

Automatic detection of damage to fishing nets is difficult to achieve with conventional machine vision algorithms. Weak contrast and poor visibility due to swirling debris and algae lead to a lot of false positive hole detections. The use of neural networks seems promising in dealing with these difficult conditions. 

Due to the large variations in water quality, net shape and foreign objects present at an aquaculture facility, we believe that using a combination of AI based and classical machine vision will give the best results. The system has to both be able to detect small holes before they represent a risk of escape and also not result in too many false positives.

The AI-based machine vision utilize Convolutional Neural Networks (CNN) and deep learning, where you present the training algorithm with tens of thousands of annotated images. Check out this nice article written by Henry Warren, that explains the CNN technology in an intuitive way.

The training takes up to 24 hours on a powerful server, and the result is a machine vision algorithm that is so efficient that it can run on a micro computer without access to the original dataset. The new machine vision has to be tested thoroughly in different conditions as there are a lot of pitfalls related to this technology. The prototype system will therefore first be used in combination with conventional inspection to prove it effectiveness.

Automatic net inspection project grant from FHF

Mohn Technology is pleased to receive 5MNOK grant from FHF – the Norwegian Seafood Research Fund to develop a new autonomous net inspection drone for the aquaculture industry.

The drone will be a new tool to help fish farmers inspect their net pens more often and effective to a lower cost than diving and conventional ROV based inspection. An autonomous underwater vehicle (AUV) is a very complex product, that will demand a lot from our developer team. Mechanical and cybernetics has to work hand in hand in order for the AUV to work efficiently and effective under difficult conditions.

The AUV will be battery powered without umbilical, to reduce the risk of entanglement in existing equipment in the facility. Navigation will be based on a mixture of machine vision, compass and IMUs (Inertial Measurement Unit). The customer will gain access to reports and status via a web portal.

We are really looking forward to start on this project, as it is both very interesting and fits our company profile well!

Many thanks to FHF for the trust and support!

Autonomous wall / net tracking achieved

Mohn Technolgy is working on an autonomous net cleaning system. Early pool testing and development is done on a simplified prototype that has an umbilical for live tracking of the process. The autonomous properties can be transferred to future prototype versions even though they will differ greatly from this early version. Both the prototype and future vessel is battery powered.

The automatic wall tracking is possible by the use of an accurate IMU (Inertial measurement unit), a stereo camera and some clever programming.