Electrolyzers-HSI: Close-Range Multi-Scene Hyperspectral Imaging Benchmark Dataset

TL;DR

This paper introduces a new benchmark dataset for recycling materials using hyperspectral imaging to support sustainable, AI-driven waste analysis.

Contribution

The novel contribution is the creation of the Electrolyzers-HSI dataset for accurate electrolyzer material classification in recycling.

Findings

The dataset includes 55 co-registered RGB and HSI data cubes for non-invasive material analysis.

State-of-the-art Transformer-based models were evaluated for robust electrolyzer identification.

The dataset and code are openly accessible to promote reproducible and scalable recycling research.

Abstract

The global challenge of sustainable recycling demands automated, fast, and accurate material detection systems that act as a bedrock for a circular economy. Integrating front-tier technologies into advanced recycling systems democratizes access to AI-driven sustainability, and transforms waste analysis from isolated research efforts into real-time, scalable industrial practice. This integration not only accelerates material recovery but also strengthens the technological backbone required to achieve large-scale recycling and alignment with the Green Deal ambitions. In response, we introduce Electrolyzers-HSI, a new multimodal benchmark dataset designed to accelerate the recovery of critical raw materials through accurate electrolyzer materials classification. The dataset comprises 55 co-registered high-resolution RGB images and hyperspectral imaging (HSI) data cubes spanning the 400–2500 nm spectral range. This enables non-invasive analysis of shredded electrolyzer samples, facilitating quantitative material classification. We evaluate various analytical methods, including state-of-the-art (SOTA) Transformer-based deep learning (DL) architectures, to validate the dataset for robust electrolyzers identification. The openly accessible dataset and codebase promote reproducible research and facilitate broader adoption of smart and sustainable E-waste recycling.

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