Author Archives: Magnus Rogne Myklebost

Finished master projects with NTNU and UiB

Brage Alvsvåg just finished his masters thesis at Department of Informatics at UiB. The title was “Improving fish detection using efficient neural networks”, and the project was done in association with Mohn Technology using our dataset. In his project, Brage tested different ways of improving our fish detection AI, with different kinds of neural networks and post-training quantization he managed to develop some promising algorithms that might proove to be very useful for us. We are looking forwards to testing out these strategies in real life operations in the coming months!

Håvard Ullaland also finished his master thesis this summer at Department of Engineering Cybernetics at NTNU. Håvards project was titled “Positioning and localization for underwater vehicle in fish pen using VSLAM”, and the work is related to our automatic net inspection tool that is supported by FHF. Håvards contribution to the project is related to navigate based on machine vision and IMU (Inertial measurement unit). The localization algoritms use the input from the machine vision and sensors to estimate where it is, and where it is going. If we succeed in only using VSLAM (Visual simultaneous localization and mapping) algoritms we can reduce the hardware cost and complexity of the system, and it will also require less setup of hardware on site before operation.

The photos above shows the stereo camera navigational points on the net to the left, and the right shows the algorithms estimated travel route along the net pen. The results are very promising.

After the masters thesis was delivered Håvard started working for us full time on the project, and will continue his work on underwater localization and navigation.  

Humpback salmon research at Tana Bru

In cooperation with Tanafisk we have installed a dual FRS camera pole in the Tana River. The humpback salmon mainly enters the rivers every other year, where they reproduce and die. The system will be used to verify the efficiency of the guide fence so that we are ready for the 2023 season where we expect a large invation of the humpack / pink salmon.

The humpback salmon is an invasive species in Norway, and the problem is spreading from the northern parts of Norway from Russia. Local fishing associations and river authorities have done a heroic job in 2021 and caught tens of thousands of fish based on volantary work.

Mohn Technology is working on a automatic fish trap that is based on our machine vision and underwater technology experience. We hope the system will be able to stop the spread of the invasive species and protect our own wild salmon, while also generating value from the catch. More info to come!

Installation in progress

We are looking for a new developer!

SØK HER / APPLY HERE! 

Vi ønsker å utvide teamet, og er på utkikk etter en engasjert softwareutvikler som ønsker varierte og spennende oppgaver.

Arbeidsoppgaver

  • Utvikle front-end grensesnitt for interaksjon med våre produkter
  • Utvikle programvareløsninger for systematisk overføring og lagring av media og event basert data
  • Oppsett av servere og system-infrastruktur
  • Arbeid med nettverksteknologi, distribuerte systemer og databaser

Dine kvalifikasjoner og egenskaper
Vi er på jakt etter personer som innehar følgende egenskaper:

  • Utdanning eller bred erfaring innen informasjonsteknologi, datateknologi eller lignende
  • Erfaring med webutvikling og rammeverk
  • Erfaring med SQL og databaser
  • JavaScript, Vue, Golang, Docker, Linux
  • God faglig kompetanse
  • Brenner for faget ditt og ønsker å skape ny teknologi
  • Strukturert, ryddig og ansvarlig
  • Evne til å jobbe selvstendig og i team

Vår tech stack:

  • Frontend: Vue, JavaScript
  • Backend: Golang, Python (FastAPI)
  • Hardware: C++, Golang

Hva kan vi tilby:

  • Jobbhverdag i et ungt og entusiastisk oppstartsmiljø
  • Varierte og spennende oppgaver og stort kreativt potensiale
  • Konkurransedyktige betingelser
  • Fleksibel arbeidshverdag

 

We wish to expand the team, and are looking for a dedicated software developer who wants varied and exiting tasks.

Work tasks

  • Develop front-end interface for interacting with out products
  • Develop software for systematic transfer and saving media and event based data
  • Setup of servers and system infrastructure
  • Work with nettwork technology, distributed systems and databases.

Your qualifications and abilities
We are looking for people with the following characteristics and abilities:

  • Education or broad experience within IT, computer science or similar
  • Experience with web development and framework
  • Experience with SQL and database
  • JavaScript, Vue, Golang, Docker, Linux
  • High level of expertise 
  • Passionate about the subject and a wish to create new technology
  • Structured, well organized and responsible
  • The ability to work independent and in a team.

Our tech stack:

  • Frontend: Vue, JavaScript
  • Backend: Golang, Python (FastAPI)
  • Hardware: C++, Golang

What we can offer:

  • A young and enthusiastic start-up enviornment
  • A wide variety of exiting tasks with a large creative potential
  • Competitive pay
  • A flexible workday

Field trial of our autonomous aquaculture drone

Mohn Technology is developing several autonomous underwater vehicles. One of them is an automatic net inspection drone for the aquaculture industry. The project is partially funded by FHF and will reduce the risk of escaped salmon by inspecting the facility in a safe, efficient and environmentally friendly way.

We have done most of the AUV (Autonomous Underwater Vehicle) in a computer simulation environment to quickly get a overview of how the AUV reacts to different scenarios, with wave motion, water current etc.

Since we do a lot of work in a simulation environment, we are able to transfer the navigation algorithms to all compatible underwater vehicles without too much extra work. In the field trial we used an existing prototype used for pelagic fisheries research, because it had a suitable stereo camera, IMU (Inertia Measurement Unit), depth sensor and onboard computer.

The umbilical is only for communication and manual override as the vehicle is battery powered. The finished product will be truly autonomous without any cable. This is done in order to reduce the risk of entanglement in a crowded net pen with numerous obstacles like ropes, sensors, cables and cleaner fish housing.

The trail was performed at an operational aquaculture facility of a Norwegian fish farmer at the west coast. Even though the facility was pretty exposed, the weather was fair and the sea was calm.

The net tracking algorithms worked well, and the test provided vital experience with real life operations in an active fish farm. We were impressed how well the algorithms manged to filter out disturbances like salmon swimming between the vehicle and the net. The salmon seemed to be very little affected by the drone, and calmly swam in close proximity to the vehicle.

Image above shows the current GUI that allows for changing distance to net and velocity during the autonomous operation. The lower visualisation shows the drone position relative to the net. The green blocks are 3D positions of the net generated from the stereo camera imagery.

As we expected, the natural state of a fish farm is more than just a orderly net. There are stitches, reinforcement ropes, equipment on the outside, algea and seaweed and fish that will interfere with a machine vision algorithm and produce false positives. This has lead us to work with AI (Artificial Intelligence) based machine vision in conjunction with a more conventional machine vision. We believe this approach will make a robust and efficient automatic inspection tool.

We are really pleased with the development progress and are exited to continue the work.

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 its effectiveness.

New research equipment for remote areas

The decline in stocks of sea trout and wild salmon on the west coast of Norway has highlighted the need for a autonomous, non-destructive sampling method. For this reason, fish biologist at NORCE LFI are currently developing a trap with the overall goal of recording wild fish for research or conservation purposes.

Mohn Technology contribute with our high resolution FRS camera applying AI to detect passing fish. This new solar powered surveillance system was just deployed at Dale, Norway. The camera system is designed to use as little power and data traffic as possible, and the solar system was assembled and tested at Mohn Technology before deployment.

The system allows the researchers at NORCE LFI to get information about fish population and migrations directly to their computers from remote areas without access to electricity, and we hope more systems like this can aid research in this important field.

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!

Master thesis with UiB

Mohn Technology has in cooperation with UiB Department of Informatics and masters student Oda Inanna Klemetsdal Stene started a project regarding AI based image segmentation. Oda will work on creating a new machine vision tool for the fisheries industry that will increase profits and reduce waste. We are looking forward to the collaboration.

If you wish to write bachelor or masters thesis with us, please contact magnus (at) mohntechnology.no