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

• URL: http://arxiv.org/abs/2505.20507v2
• Date: Thu, 05 Jun 2025 10:02:51 GMT
• Title: Electrolyzers-HSI: Close-Range Multi-Scene Hyperspectral Imaging Benchmark Dataset
• Abstract summary: We introduce textbfElectrolyzers-HSI, a novel multimodal benchmark dataset designed to accelerate the recovery of critical raw materials.<n>The dataset comprises 55 co-registered high-resolution RGB images and hyperspectral imaging (HSI) data cubes spanning the 400--2500 nm spectral range.<n>This enables non-invasive spectral analysis of shredded electrolyzer samples, supporting quantitative and qualitative material classification and spectral properties investigation.
• Score: 8.578006502996516
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• MWIR-4-Plastic: The Identification of Complex End-of-Life Industrial Plastic using Mid-wave Infrared Hyperspectral Imaging and Machine Learning [7.766403812781242]
  Black industrial plastics are underrepresented, while most classification pipelines depend on manual region selection and rule-based spectral matching.<n>We develop a multi-modal spectral-spatial framework that integrates foreground isolation, pixel-wise classification, and object-level majority voting.<n>The study establishes the first comprehensive benchmark using nine processing methods, including chemometric, machine learning, and DL architectures.
  arXiv  Detail & Related papers  (2026-08-28T21:24:54Z)
• Multi-Modal Semantic Segmentation of Electrolyzer Components for Sustainable Hydrogen Technologies: A Dual-Branch Deep Learning Approach [1.8343941786735474]
  Hyperspectral-RGB Electrolyzer Materials Network (HREM-Net) combines hyperspectral imaging (HSI) and RGB images for electrolyzer material segmentation.<n>With an adaptive gated cross-modal fusion module and composite loss function, HREM-Net achieves a mean class accuracy of 91.66% and a mean Intersection over Union (mIoU) of 0.82.<n>This work poses its potential as an industrial application to improve electrolyzer efficiency, thereby improving the predictive maintenance of hydrogen production.
  arXiv  Detail & Related papers  (2026-07-17T15:34:20Z)
• FLORO: A Multimodal Geospatial Foundation Model for Ecological Remote Sensing Across Sensors and Scales [65.4821703903285]
  FLORO is a multimodal foundation model designed to learn transferable representations from a small but highly diverse remote sensing corpus.<n> FLORO is pretrained using masked autoencoding on a heterogeneous combination of Sentinel-1, Sentinel-2, SkySAT imagery, elevation, and UAV-derived data.<n>We evaluated FLORO on the PANGAEA benchmark under a frozen-encoder protocol across scene classification, segmentation, and regression tasks.
  arXiv  Detail & Related papers  (2026-05-27T08:55:54Z)
• Amped: Adaptive Multi-stage Non-edge Pruning for Edge Detection [53.59823657644554]
  We propose an Adaptive Multi-stage non-edge Pruning framework for Edge Detection(Amped)<n>Amped identifies high-confidence non-edge tokens and removes them as early as possible to substantially reduce computation.<n>To mitigate the structural complexity of existing edge detection networks, we introduce a simple yet high-performance Transformer-based model, termed Streamline Edge Detector(SED)
  arXiv  Detail & Related papers  (2026-03-29T12:26:35Z)
• CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation -- A Deep Learning Framework for Smart Manufacturing [51.56484100374058]
  CLAIRE is a hybrid end-to-end learning framework that integrates unsupervised deep representation learning with supervised classification for intelligent quality control in smart manufacturing systems.<n>It employs an optimized deep autoencoder to transform raw input into a compact latent space, effectively capturing the intrinsic data structure while suppressing irrelevant or noisy features.<n>The proposed framework highlights the potential of integrating explainable AI with feature-aware regularization for robust fault detection.
  arXiv  Detail & Related papers  (2026-03-06T15:11:58Z)
• An Integrated System for WEEE Sorting Employing X-ray Imaging, AI-based Object Detection and Segmentation, and Delta Robot Manipulation [0.0]
  Battery recycling is becoming increasingly critical due to the rapid growth in battery usage and the limited availability of natural resources.<n>As battery energy densities continue to rise, improper handling during recycling poses significant safety hazards, including potential fires at recycling facilities.<n>We present our novel approach which integrates a specialized X-ray transmission dual energy imaging subsystem with advanced pre-processing algorithms.
  arXiv  Detail & Related papers  (2025-12-05T10:36:33Z)
• Hyperspectral Imaging [49.45523645429475]
  Hyperspectral imaging (HSI) is an advanced sensing modality that simultaneously captures spatial and spectral information.<n>This Primer presents a comprehensive overview of HSI, from the underlying physical principles and sensor architectures to key steps in data acquisition, calibration, and correction.
  arXiv  Detail & Related papers  (2025-08-11T15:47:24Z)
• Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks [62.12107686529827]
  This article highlights a significant shift towards leveraging quantum computing techniques in processing large volumes of remote sensing data. The proposed Quanv4EO model introduces a quanvolution method for preprocessing multi-dimensional EO data. Key findings suggest that the proposed model not only maintains high precision in image classification but also shows improvements of around 5% in EO use cases.
  arXiv  Detail & Related papers  (2024-07-24T09:11:34Z)
• Hyperspectral Dataset and Deep Learning methods for Waste from Electric and Electronic Equipment Identification (WEEE) [0.0]
  We evaluate the performance of diverse deep learning architectures for hyperspectral image segmentation. Results show that incorporating spatial information alongside spectral data leads to improved segmentation results. We contribute to the field by cleaning and publicly releasing the Tecnalia WEEE Hyperspectral dataset.
  arXiv  Detail & Related papers  (2024-07-05T13:45:11Z)
• PCB-Vision: A Multiscene RGB-Hyperspectral Benchmark Dataset of Printed Circuit Boards [11.658030498915535]
  'PCB-Vision' is a pioneering RGB-hyperspectral printed circuit board (PCB) benchmark dataset, comprising 53 RGB images of high spatial resolution paired with their corresponding high spectral resolution hyperspectral data cubes in the visible and near-infrared (VNIR) range. We provide extensive statistical investigations on the proposed dataset together with the performance of several state-of-the-art (SOTA) models, including U-Net, Attention U-Net, Residual U-Net, LinkNet, and DeepLabv3+.
  arXiv  Detail & Related papers  (2024-01-12T12:00:26Z)
• ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data Fusion [54.668445421149364]
  Deep learning-based hyperspectral image (HSI) super-resolution aims to generate high spatial resolution HSI (HR-HSI) by fusing hyperspectral image (HSI) and multispectral image (MSI) with deep neural networks (DNNs) In this letter, we propose a novel adversarial automatic data augmentation framework ADASR that automatically optimize and augments HSI-MSI sample pairs to enrich data diversity for HSI-MSI fusion.
  arXiv  Detail & Related papers  (2023-10-11T07:30:37Z)
• Autonomous Point Cloud Segmentation for Power Lines Inspection in Smart Grid [56.838297900091426]
  An unsupervised Machine Learning (ML) framework is proposed, to detect, extract and analyze the characteristics of power lines of both high and low voltage. The proposed framework can efficiently detect the power lines and perform PLC-based hazard analysis.
  arXiv  Detail & Related papers  (2023-08-14T17:14:58Z)
• Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra [0.0]
  We showcase machine learning methods to classify the ECMs of 9,300 impedance spectra provided by QuantumScape for the BatteryDEV hackathon. A key remaining challenge is the identifiability of the labels, underlined by the model performances and the comparison of misclassified spectra.
  arXiv  Detail & Related papers  (2023-02-07T10:08:35Z)
• Supervised classification methods applied to airborne hyperspectral images: Comparative study using mutual information [0.0]
  This paper investigates the performance of four supervised learning algorithms, namely, Support Vector Machines SVM, Random Forest RF, K-Nearest Neighbors KNN and Linear Discriminant Analysis LDA. The experiments have been performed on three real hyperspectral datasets taken from the NASA's Airborne Visible/Infrared Imaging Spectrometer Sensor AVIRIS and the Reflective Optics System Imaging Spectrometer ROSIS sensors.
  arXiv  Detail & Related papers  (2022-10-27T13:39:08Z)
• RGB-X Classification for Electronics Sorting [10.409080299411645]
  This work introduces RGB-X, a multi-modal image classification approach, that utilizes key features from external RGB images with those generated from X-ray images to accurately classify electronic objects. We present a novel way of creating a synthetic dataset using domain randomization applied to the X-ray domain. The combined RGB-X approach gives us an accuracy of 98.6% on 10 generations of modern smartphones, which is greater than their individual accuracies of 89.1% (RGB) and 97.9% (X-ray) independently.
  arXiv  Detail & Related papers  (2022-09-08T00:33:00Z)
• MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis [72.85526892440251]
  We introduce MetaGraspNet, a large-scale photo-realistic bin picking dataset constructed via physics-based metaverse synthesis. The proposed dataset contains 217k RGBD images across 82 different article types, with full annotations for object detection, amodal perception, keypoint detection, manipulation order and ambidextrous grasp labels for a parallel-jaw and vacuum gripper. We also provide a real dataset consisting of over 2.3k fully annotated high-quality RGBD images, divided into 5 levels of difficulties and an unseen object set to evaluate different object and layout properties.
  arXiv  Detail & Related papers  (2022-08-08T08:15:34Z)
• Remote Sensing Image Classification using Transfer Learning and Attention Based Deep Neural Network [59.86658316440461]
  We propose a deep learning based framework for RSISC, which makes use of the transfer learning technique and multihead attention scheme. The proposed deep learning framework is evaluated on the benchmark NWPU-RESISC45 dataset and achieves the best classification accuracy of 94.7%.
  arXiv  Detail & Related papers  (2022-06-20T10:05:38Z)
• Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review [85.36368666877412]
  hyperspectral (HS) remote sensing (RS) imaging has provided a significant amount of spatial and spectral information for the observation and analysis of the Earth's surface. The recent advancement and even revolution of the HS RS technique offer opportunities to realize the full potential of various applications. Due to the maintenance of the 3-D HS inherent structure, tensor decomposition has aroused widespread concern and research in HS data processing tasks.
  arXiv  Detail & Related papers  (2022-05-13T00:39:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.