COVER STORY SPONSORED BY JENSEN March of the machines
Jensen’s partnership with ‘laundry nerds’ Inwatec was the catalyst for revolutionary AI development for laundries – and successive developments are learning to be even more intelligent
How much AI does a laundry need?
Artificial Intelligence (AI) is creating a real buzz and making a huge difference in all kinds of industries, most notably in laundries. The automation of the soil sort area, for instance, is now taken care of by robots utilising AI. It is the safest, most hygienic and most sustainable way to sort soiled linen – and one day soon, it will be the most common way as well.
About AI
Artificial Intelligence refers to the development of computer systems and software that can perform tasks typically requiring human intelligence. These tasks include problem-solving, learning from experience, understanding natural language, recognizing patterns, and making decisions. AI systems are designed to mimic or simulate human cognitive functions, such as perception, reasoning, problem-solving, and decision-making, often with the goal of automating processes, improving efficiency, and handling complex tasks that would be challenging or time-consuming for humans to perform. AI encompasses various subfields, including machine learning, natural language processing, computer vision, and robotics, and it continues to advance and evolve with the goal of creating systems that can exhibit human-like or superhuman intelligence in specific domains.
ROBOT TAKEOVER: THOR eliminates the need for manual sorting by automatically separating laundry items on the soiled side. Over 100 robots are already in use worldwide
Systems from Inwatec utilising AI
The ODIN.Xray scanner is capable of scanning and subsequently rejecting garments containing harmful foreign objects, such as pens or needles. To achieve the best possible results, cutting-edge Artificial Intelligence software is utilized to continuously train the machine and optimize its parameters according to the
specific requirements of each laundry. The quality of detection ultimately depends on the types of garments being scanned. We have collected millions of images, which are segmented into samples for training a neural network with approximately five million of those samples. Our goal is to provide the neural network with enough samples to have seen most, if not all, items that pass through a laundry. Neural networks excel at identifying items they have encountered before. Hence, we aim to provide as many examples as possible of what accepted items should look like (e.g., buttons, zippers) and what foreign, unwanted objects (e.g., knives, scalpels, scissors) should look like. Detecting metal is relatively straightforward, but identifying plastic items is more challenging. A conventional metal detector, as used in most laundries, would only reject a knife but not a plastic pen.
FUTURE FIT: With the help of Inwatec’s robotics and automation solutions, laundries become fit for the future and thus manage to remain competitive
8 LCN | October 2024
The initial version of ODIN did not rely on AI. By transitioning to normal computer vision with AI, we have increased the detection rate to up to 99%. Taking a picture with an X-Ray scanner is not a complex task, but finding all the undesired
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