March of the machines

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.

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.

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 items while accepting items belonging to a garment is. The AI in ODIN ensures that the system can differentiate between laundry items that need to be rejected due to the presence of unwanted objects.

The HEIMDAL.Camera is built upon Artificial Intelligence to sort products based on their visual characteristics, such as colors, patterns, textures, and even size. Coupled with intelligent software, HEIMDAL can distinguish between different types of laundry articles. Each project receives its unique set of data, which requires training for the specific sorting composition and product portfolio. Currently, we have various systems in operation for workwear, linen, and mixed products. Even without RFID, HEIMDAL can achieve higher sorting accuracy than manual sorting.

The input for HEIMDAL is also an image, but unlike the X-Ray image, it is a real color photograph. The system is similar to ODIN in that it employs a neural network to create a fingerprint of the item, enabling precise identification, distinguishing between items such as small towels and large towels.

Comparison with our daily dose of AI: ChatGPT

ChatGPT is trained using stored, collected data that is transformed into responses through neural networks. This process mirrors how AI functions in laundries. The key difference lies in our use of humanlabeled images, which are defined and entered into the system by humans to ensure the accuracy of the data fed into the neural network. We collaborate closely with software engineers to refine the models, create the correct dataset, and establish an effective training process right from the outset. When training the AI for ODIN, it may occasionally make incorrect choices or mistakes.

The system can only improve when models are trained through neural networks, not solely relying on predictions - which can be erroneous. Using a neural network is, by definition, an AI approach – successfully applied in ChatGPT, a number of industrial applications in all kinds of industries and in the sorting area of many laundries around the world.

Without AI, it would merely function as a photographic tool, necessitating a substantial team of operators to handle the detection of items. Thanks to AI, the process of sorting soiled textiles is automated to a great extent, reducing the need for human intervention in the removal of detected unwanted items.

Bridging sustainability through the combination of new technology and old technology

AI is not going to replace everything. Like many other things, it works best in combination with existing technology, such as X-ray technology. On November 8, 1895, Wilhelm Conrad Röntgen discovered X-rays by coincidence when he was experimenting in the dark and suddenly saw the bones in his hand. Ever since then, we have been familiar with this technology from our visits to the doctor. Today, the X-ray scanner is indispensable for laundries. In addition to all the reasons mentioned, such as detecting and rejecting foreign, unwanted objects such as pens or needles, it is also important to protect the employees working in a laundry. A pen can get stuck in a tunnel washer and ruin a load of white linen, which can obviously cause great frustration for the laundry. If the laundry is processing medical linen, foreign objects such as scalpels or a syringe can be forgotten in the garments and injure or infect the operators. The use of X-ray technology in laundries therefore extends the life of textiles and machinery and provides a safe working environment for operators in the laundry. This is why robots are making laundries smarter, safer and more sustainable.

Look into the future

Developments are progressing rapidly, and AI will further increase the performance of laundries. However, replacing humans still proves to be a challenging task, given the smart combination of body and brain. Robots, on the other hand, only do what they are told to do, and that is why the best people are needed to build intelligent robots. With their brains at hand, a laundry can start working with artificial intelligence.

AI means the construction of a computer designed to function like a small brain. Teaching it involves feeding a significant amount of relevant data paired with the correct solutions. The computer gets smarter as it learns more types of patterns. A notable example is the HEIMDAL. Camera, a system dedicated to visual sorting and primarily used for unmarked garments in laundry processes. Recent advancements in AI mostly occur when we use particular input data to create a quick and straightforward response. If an ordinary person can perform a mental task in less than a second, it is probable that we can automate it now or in the near future with the assistance of AI. These machines enable laundries to handle more complex and specialized tasks. Robots take over repetitive duties, allowing human operators to manage customer-related responsibilities.

The laundry industry is stepping into a new era where technology can manage the majority of production processes without human involvement. In collaboration with our partner Inwatec, the Jensen-Group is actively shaping the future of the laundry industry. These cutting-edge innovations in robotics and artificial intelligence increase both, the IQ and productivity in laundries.