Summary is AI-generated, newsdesk-reviewed
  • Innodisk and EverestLabs launch AI recycling solution for efficient material identification and sorting.
  • APEX-P200 edge AI system enhances real-time, 3D analysis with robust, industrial-grade performance.
  • Collaboration aims to improve recovery rates, operational efficiency, and reduce waste stream contamination.

A pioneering collaboration between Innodisk, RealSense, and EverestLabs aims to transform recycling processes through an advanced AI vision solution. The collaboration seeks to enhance material sorting on recycling facility lines by combining Innodisk's APEX-P200 edge AI system, RealSense depth cameras, and the RecycleOS platform developed by EverestLabs.

Innodisk, recognized for its AI solutions, announced this partnership to deliver a scalable, real-time solution engineered for the demands of industrial recycling environments. These environments require systems capable of making swift decisions while maintaining resilience to environmental factors and ensuring continuous operation.

Challenges in Industrial Recycling

Material Recovery Facilities (MRFs), alongside other recycling operators and waste management companies, are under pressure to enhance recovery rates, improve material quality, and boost labor efficiency. Meeting these goals is complicated by strict regulatory standards. Traditional sorting systems struggle with complex and diverse waste streams, often leading to contamination and inefficiencies.

Technology Integration for Enhanced Sorting

The innovative solution centers around Innodisk’s rugged APEX-P200 edge AI system

The innovative solution centers around Innodisk’s rugged APEX-P200 edge AI system, integrated with RealSense's depth camera and EverestLabs's RecycleOS platform. This setup facilitates precise material identification and sorting, directly on the plant floor. Real-time multi-camera 3D analysis is enabled by a 13th-Gen Intel® Core™ i7 processor and an NVIDIA RTX 2000 Ada GPU, allowing for rapid AI inference and on-site decision-making independent of cloud support.

Victor Le, President of Innodisk USA Corporation, stated, "Advancing sustainable recycling requires an edge AI solution that provides reliability in real-world industrial conditions. Working with EverestLabs and RealSense, we're delivering platforms that run continuously on production lines, helping facilities improve both recovery rates and environmental impact."

Precision and Reliability in Real-Time Sorting

RealSense’s depth cameras provide factory-calibrated 3D depth alongside color imaging, crucial for measuring and segmenting mixed materials transported on fast-moving conveyors. This precision is achieved through the potent combination of spatial data and robust processing by the APEX-P200, facilitating consistent real-time sorting.

Nadav Orbach, CEO of RealSense, emphasized, “Getting recycling right comes down to reliable perception in harsh, high-speed conditions. Together with Innodisk and EverestLabs, we’re bringing production-grade 3D sensing to the line so operators can increase purity and recovery without slowing throughput.”

AI-Driven Automation in Recycling

EverestLabs incorporates the APEX-P200 and RealSense technology into its AI-powered recycling systems

EverestLabs incorporates the APEX-P200 and RealSense technology into its AI-powered recycling systems. These systems deploy advanced Vision AI models, enhancing MRFs' operational efficiency, minimizing contamination, and enabling scalable AI-driven automation.

Ravichandra Malapati, Principal Engineer at EverestLabs, noted, "Our systems operate in some of the world’s toughest industrial environments and require embedded-grade reliability, high-accuracy Physical AI, and real-time responsiveness. Innodisk’s APEX-P200 meets all three requirements."

The partnership underscores Innodisk's collaborative strategy, working with market specialists and sensing innovators to deliver flexible edge AI platforms. This strategy supports the rapid deployment of production-ready solutions tailored to industry-specific needs.

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