AI Detects Contaminated Construction Wood with 91% Accuracy

An innovative AI system has been developed to detect contamination in construction and demolition wood waste—one of the most challenging and overlooked waste streams in the building industry. This new solution uses advanced deep learning models trained on a large real-world dataset of contaminated wood, achieving 91% accuracy in identifying various contamination types using just standard RGB images.

The system is designed to recognize visible signs of contamination, including paint, metal fragments, adhesives, chemical stains, and other materials that typically make wood unsuitable for recycling. By analyzing images in real time, the AI can help waste processors automatically sort contaminated wood, improving safety, reducing manual labor, and increasing recovery of reusable materials.

Unlike traditional sorting methods, which are time-consuming and inconsistent, this AI-powered approach offers a scalable, reliable alternative. It can be deployed in sorting lines, aerial drones, or handheld scanning devices, allowing on-site decisions to be made faster and with greater accuracy—especially in high-volume or hazardous environments.

This development represents a significant step toward automated resource recovery and contributes directly to circular economy initiatives by enabling more wood to be recycled rather than discarded. It also opens the door for similar AI-driven solutions in other specialized waste streams, where manual sorting is impractical or too costly.

By leveraging image-based learning and focusing on real-world data, this system demonstrates how AI can play a transformative role in sustainable construction, recycling innovation, and global waste reduction efforts.