Category Archives: Aquaculture

We are looking for summer interns and academic collaboration!

Mohn Technology is expanding our investment in self-developed underwater products for fisheries, aquaculture and research purposes. Our office is located at Laksevåg, Bergen, with a good view of the city and the city mountains. Proximity to the sea and a good workshop enable us to create or modify prototypes, as well as test equipment in the sea right outside our window.

The work will have a major focus on practical use of the subject, with hands-on work, testing and implementation. You will receive close follow-up from our engineers who have good experience with image processing, cybernetics and product development. At the same time, as part of a small company, you will be given a lot of responsibility and will have a steep learning curve.

We are currently looking for skilled students who are passionate about their work, to join our small but resourceful team in 2023. We would like to combine a summer job with a subsequent project and master’s thesis, and are also looking for graduates for a full-time position. For summer internships, fourth year masters students and second year bachelor students will be prioritised. Info about previous student relations can be found here!

Typical tasks

  • Develop control algorithms for ROV/AUV
  • Use of mono/stereo camera to control/navigate ROV/AUV
  • Use of a stereo camera for object recognition and estimation of sizes
  • Use deep learning and develop/train an algorithm for recognizing certain objects
  • Develop an intuitive GUI for underwater drones, for both image analysis and control.
  • Frontend development with close customer contact

Learning outcome for the student (when writing a master’s thesis)

  • Development of modern camera and image technology that is relevant to business
  • Experience with control systems for underwater robots (ROV/AUV)
  • Practical experience with the development of products where camera systems interact with mechatronics
  • Gain experience with the development and practical use of deep learning, especially in image segmentation
  • Experience with testing prototypes

Our wish list:

  • Experience with and interest in machine vision and relevant libraries
  • Experience with or interest in Deep learning and CNN.
  • Experience with or interest in Javascript (Vue.js)
  • C/C++ / Golang / Python
  • Practical and interested in working closely with prototypes
  • Interest in or experience with ROS (Robot Operating System)

Contact magnus (at)!

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.

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.