Eureka moment for Scottish company bringing deep learning to ancient art of weighing ships

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A consortium of researchers in Scotland has developed new artificial intelligence (AI) technology that will modernise the way shipping vessels are weighed and checked for stability, a process still based on principles formulated by Greek scientist Archimedes more than 2,000 years ago.

Naval architecture firm Tymor Marine and the University of Edinburgh, with support and funding from CENSIS – Scotland’s innovation centre for sensing, imaging, and Internet of Things (IoT) technologies – have created a machine vision tool, powered by deep learning, that will automate and more accurately undertake the reading of draught marks on ships.

Draught marks – numbers marked in increments on the side of vessels to indicate how much of the ship is submerged – are currently measured and recorded by eye from the quay or a boat, similar to the way they have been for more than two millennia.

However, the measurements are often open to interpretation – waves, faded markings, lighting, and marine growth are just some of the factors that can lead to different readings being taken from the same vessel. Mariners also have to check the marks on both sides of a ship, which can take hours, requires a boat, and involves health and safety risks.

Accurate draught readings are critical for ensuring a ship’s stability, indicating how much cargo it is carrying and what depths it can safely navigate. The readings are also checked by port authorities to ensure vessels are complying with local limits and regulations.

The technology uses algorithms applied to video recordings of ships to accurately identify where the water line reaches on a ship’s hull. Tymor Marine and the University of Edinburgh will continue to develop the technology, with the aim of creating a smartphone app that allows seafarers to record draught marks and upload them to the cloud for real-time readings.

Rosie Clegg, naval architect at Tymor Marine, said: “We had been trying to develop this technology for some time, but quickly found there was no off-the-shelf software. Through CENSIS, we found the expertise we needed at the University of Edinburgh to develop our own technology and bring innovation to what is, broadly speaking, a traditional industry.

“Over the last twelve weeks, we have been able to prove that the concept behind the technology is feasible. Now we will focus on its different elements, train it with data we are now capturing with each visit to a vessel, and begin taking it to a commercial level. We are also exploring the possibility of applying it to drones, which would make the process even safer.

“Finding people with the right skills to help us innovate is tricky for a company of our size – we didn’t have deep learning expertise in house. Without CENSIS’s support, this project would not have happened and we are highly encouraged by the results so far.”

Dr. Hakan Bilen, reader in the School of Informatics at the University of Edinburgh, added: “When researchers were developing AI in the early years, they thought it would easily solve visual tasks that we do effortlessly like recognising digits and estimating waterline and struggle with more complex situations, such as playing a game of chess. However, the opposite has turned out to be the case and it is the seemingly simple tasks that we are still finessing.

“The algorithm we have created for Tymor Marine has been built on the recent advances in deep neural networks. The model takes in a video showing a ship’s hull and identifies where digits on the side of a vessel intersects the water line in a variety of different scenarios. We are continuing to build the database by introducing more manual annotations for training and also to improve various components in the method, which should only make it more accurate in the future.”

Corinne Critchlow-Watton, project manager at CENSIS, said: “It is incredible to think that the worldwide shipping industry still relies on principles developed in ancient Greece for such an important part of how it operates. Machine vision could bring a more accurate, consistent, and safer approach to stability and weight checks for vessels, which can only be hugely positive for the sector.”

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