Traditional recycling methods often suffer from inefficiencies, contamination, and high operational costs. The introduction of Machine Vision Recycling Systems (MVRS) offers a transformative solution to these problems, heralding a new era in urban waste management.
A machine vision recycling system utilises computer vision to identify and sort recyclable materials. This system operates through high-resolution cameras and sophisticated algorithms that can distinguish between different types of waste with remarkable accuracy. As waste moves along a conveyor belt, the machine vision system scans and categorises each item, directing it into the appropriate recycling stream. This process significantly reduces contamination and increases the purity of recyclable materials, enhancing the overall efficiency of recycling operations.
By automating the sorting process, MVRS reduces the reliance on manual labour, which is often prone to errors, accidents and inefficiencies. It also enables cities to handle larger volumes of waste without proportionally increasing costs or resource use. Moreover, the precision of machine vision ensures that more materials are correctly recycled, thereby reducing the environmental impact of waste and promoting sustainable urban living.
Another critical aspect of machine vision recycling systems is their adaptability. These systems can be programmed to recognise a wide variety of materials, from plastics and metals to paper and glass, making them versatile and highly effective. Additionally, continuous improvements in AI and machine learning mean that these systems can evolve and become more efficient over time, further enhancing their contribution to urban sustainability.
Applies AI and robotics to modernize recycling infrastructure.
TOMRA
Norway · Company
Provides sensor-based sorting solutions for the food, recycling, and mining industries.
Finland · Company
A supplier of AI-based robotic waste sorting systems, now part of Terex.
Provides AI waste analytics to monitor and audit waste flows.
United Kingdom · Startup
Brings advanced machine learning, computer vision, and robotics to the waste management industry.
United States · Startup
Develops RecycleOS, an AI operating system for recycling plants.
France · Company
Manufacturer of intelligent optical sorting equipment for waste treatment.
CleanRobotics
United States · Startup
Creator of TrashBot, a smart waste bin that uses AI and computer vision to sort waste at the point of disposal.
Poland · Startup
An IoT smart waste bin that automatically recognizes, sorts, and compresses waste using AI-based object recognition.
News
More efficient recycling with AI: Joint project develops automated sorting solution for bulky wasteDFKI (German Research Center for Artificial Intelligence) · Jun 15, 2025
The SmartRecycling-Up project, led by DFKI, has developed an AI-based concept that automates the sorting of bulky and construction waste using cranes and intelligent sensors, successfully tested at the ASO GmbH facility.
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More efficient recycling with AI: Joint project develops automated sorting solution for bulky wasteDFKI (German Research Center for Artificial Intelligence) · Jun 15, 2025
The SmartRecycling-Up project, led by DFKI, developed an AI-based concept that automates the sorting of bulky and construction waste using cranes or excavators equipped with intelligent sensors.
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The Future of Recycling: Robots on the Risewaste-management-world.com
Recycling processes need to be cost- and time-efficient. Data-driven technology is therefore becoming increasingly interesting for the recycling industry.
Article
Optimization of an Intelligent Sorting and Recycling System for Solid Waste Based on Image Recognition Technologyonlinelibrary.wiley.com
In this paper, the technique of image recognition algorithm is used to conduct an in-depth study and analysis of the intelligent classification and recycling system of solid waste and to optimize the design of its system. The network structure and detection principle of the YOLO target detection algorithm based on convolutional neural nets are analysed, images of construction solid waste are collected as a dataset, and the image dataset is expanded using data enhancement techniques, and the target objects in the dataset are labelled and used to train their own YOLO detection models. To facilitate testing the images and to design a YOLO algorithm-based construction solid waste target detection system. Using the detection system for construction solid waste recognition, the YOLO model can accurately detect the location, class, and confidential information of the target object in the image. Image recognition is a technique to recognize images by capturing real-life images through devices and performing feature extraction, and this technique has been widely used since its inception. The deep learning-based classification algorithm for recyclable solid waste studied in this paper can classify solid waste efficiently and accurately, solving the problem that people do not know how to classify solid waste in daily life. The convolutional layer, pooling layer, and fully connected layer in a convolutional neural network are responsible for feature extraction, reducing the number of parameters, integrating features into high-level features, and finally classifying them by SoftMax classifier in turn. However, the actual situation is intricate and often the result is not obtained as envisioned, and the use of migration learning can be a good way to improve the overfitting phenomenon. In this paper, the combination of lazy optimizer and lookahead can improve the generalization ability and fitting speed as well as greatly improve the accuracy and stability. The experimental results are tested, and it is found that the solid waste classification accuracy can be as high as 95% when the VGG19 model is selected and the optimizer is combined.
