电解槽-HSI：近景多场景高光谱成像基准数据集
Electrolyzers-HSI: Close-Range Multi-Scene Hyperspectral Imaging Benchmark Dataset
AI总结：
本文提出电解槽-HSI数据集，用于加速关键原材料回收，通过准确的电解槽材料分类支持定量和定性分析，评估了多种机器学习和深度学习方法以优化材料识别效率。
摘要：
全球可持续回收的挑战需要自动化、快速且准确的最新材料检测系统，作为循环经济的基础。为扩大回收努力并促进绿色协议，我们引入了电解槽-HSI，一个新型多模态基准数据集，旨在通过准确的电解槽材料分类加速关键原材料的回收。该数据集包含55张共注册的高分辨率RGB图像和覆盖400-2500 nm波长范围的高光谱成像（HSI）数据立方体，产生超过420万个像素向量和424,169个标记像素。这支持对粉碎电解槽样本的无损光谱分析，支持定量和定性材料分类和光谱特性研究。我们评估了一套基线机器学习（ML）方法和最新基于变压器的深度学习（DL）架构，包括视觉Transformer、SpectralFormer和多模态融合Transformer，以研究在部署变压器进行材料识别时的架构瓶颈，以进一步优化效率。我们实现了零样本检测技术和跨像素级预测的多数投票，以建立对象级分类的鲁棒性。遵循FAIR数据原则，电解槽-HSI数据集和配套代码库在https://github.com/hifexplo/Electrolyzers-HSI和https://rodare.hzdr.de/record/3668上公开，支持可重复研究并促进更广泛采用智能可持续电子废弃物回收解决方案。
英文摘要：
The global challenge of sustainable recycling demands automated, fast, and accurate, state-of-the-art (SOTA) material detection systems that act as a bedrock for a circular economy. Democratizing access to these cutting-edge solutions that enable real-time waste analysis is essential for scaling up recycling efforts and fostering the Green Deal. In response, we introduce \textbf{Electrolyzers-HSI}, a novel 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, yielding over 4.2 million pixel vectors and 424,169 labeled ones. This enables non-invasive spectral analysis of shredded electrolyzer samples, supporting quantitative and qualitative material classification and spectral properties investigation. We evaluate a suite of baseline machine learning (ML) methods alongside SOTA transformer-based deep learning (DL) architectures, including Vision Transformer, SpectralFormer, and the Multimodal Fusion Transformer, to investigate architectural bottlenecks for further efficiency optimisation when deploying transformers in material identification. We implement zero-shot detection techniques and majority voting across pixel-level predictions to establish object-level classification robustness. In adherence to the FAIR data principles, the electrolyzers-HSI dataset and accompanying codebase are openly available at https://github.com/hifexplo/Electrolyzers-HSI and https://rodare.hzdr.de/record/3668, supporting reproducible research and facilitating the broader adoption of smart and sustainable e-waste recycling solutions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
