Automatic Eyes of the Sorting Plant – How Computer Vision Supports Recycling

From year to year, the pressure to keep waste out of landfills and return it back into circulation is growing. EU regulations related to the Circular Economy increasingly require companies to recover materials, while rising labor costs make manual waste sorting less and less viable.

On many sorting lines, people still manually separate plastic from glass or paper from metal, it is a fast, repetitive work that offers limited precision. This is where Computer Vision (CV) enters the stage, often referred to as the “automatic eyes of the sorting plant.”

AI-powered systems can recognize and classify waste faster, more accurately, and more cost-effectively than humans. And importantly, they learn the same things we once taught people, e.g. how to distinguish plastic from metal, clean material from contaminated and recyclable packaging from worthless trash.

Why Waste Sorting Is a Challenge for Computer Vision?

At first glance, recognizing a bottle or a can seems to be easy. In practice, it’s one of the most difficult tasks of Computer Vision. Waste almost never looks “textbook-perfect.” It’s often crumpled, dirty, partially covered with labels or poorly lit.

On top of this, material diversity is huge. The same type of plastic can look completely different depending on the manufacturer and color. Sometimes colored glass resembles plastic and cardboard coated with foil can confuse even experienced plant workers.

That’s why waste sorting is one of the most demanding environments for AI systems. Despite all of this, advances in machine learning have made the technology surprisingly effective.

AI-powered detections and automated sorting with robotics and optical sorters

Source: https://recycleye.com/recycleye-stories/

Modern Computer Vision Techniques in Recycling

Material Recognition

The foundation of today’s CV systems are deep learning neural networks (e.g., CNN, EfficientNet, YOLOv8). They classify objects into categories such as plastic, glass, metal or paper.

Increasingly, these systems are supported by multispectral cameras, which “see” in the near-infrared (NIR) range, allowing them to differentiate PET from PVC, even when they look similar to the human eye.

Material Quality Classification

In recycling, material type isn’t everything – quality matters too. CV identifies whether a PET bottle is clean or covered with labels and residue.

Depending on quality, the system assigns grades such as A, B, or C. This directly impacts profitability, as cleaner fractions have significantly higher resale value.

Anomaly Detection & Few-Shot Learning

Modern models no longer need thousands of images of each waste type. Thanks to anomaly detection and few-shot learning, a few photos of a new material are enough for the system to learn to recognize it.

This makes it easy for sorting facilities to extend the range of detectable materials without rebuilding their datasets from scratch.

Case Example – Automated Sorting Line

In a typical solution, a camera mounted above the conveyor belt records every object. The CV system analyzes the image in real time and assigns the material to the correct category. Next, a pick-and-place robot moves the item into the appropriate container.

What did we get? Classification accuracy exceeded 95%, fraction purity increased by approximately 30% and 20% reduction in manual labor costs.

This means ROI in under one year, driven largely by the higher resale value of secondary materials and less waste going to landfill.

One example of companies implementing such solutions is Bine.world. This is a platform combining Computer Vision with tools for managing waste flow, proving that CV is becoming part of larger Circular Economy systems.

Business and Environmental Benefits

Implementing Computer Vision in recycling translates into clear numbers. Fewer sorting errors equals lower operational costs, cleaner fractions means higher resale prices and last but not least the more recovered materials the less landfill waste.

It also helps companies comply with increasing EU regulatory requirements regarding documented recycling and recovery levels.

In the long term, automated sorting becomes a strategic element of the Circular Economy, combining business profitability with environmental responsibility.

Hands-On Demo – See How It Works

The simplest way to see Computer Vision in action is to try an online mini-demo. Upload a photo of a waste, e.g. a bottle, can or piece of cardboard, and the system will analyze it and classify the material. Results look like “metal – 99%” or “paper – 95%” with optional A–C quality grades.

Companies can also request a report based on multiple images, including recommendations for integrating AI with their existing sorting line. It’s a simple, tangible first step toward fully automated sorting.

Conclusion & Series Preview

Computer Vision in recycling is more than a technological curiosity, it is practical support for the Circular Economy, combining technology, ecology, and solid business foundations.

With modern algorithms and declining implementation costs, the “automatic eyes of the sorting plant” are becoming a new industry standard.

Next in the series: How Computer Vision supports the energy sector and critical infrastructure inspections from power lines to wind turbines.