Addressing the critical theme of recycling electronic waste (E-waste), this contribution is dedicated to developing advanced automated data processing pipelines as a basis for decision-making and process control. Aligning with the broader goals of the circular economy and the United Nations (UN) Sustainable Development Goals (SDG), our work leverages non-invasive analysis methods utilizing RGB and hyperspectral imaging data to provide both quantitative and qualitative insights into the E-waste stream composition for optimizing recycling efficiency. In this paper, we introduce 'PCB-Vision'; 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. Grounded in open science principles, our dataset provides a comprehensive resource for researchers through high-quality ground truths, focusing on three primary PCB components: integrated circuits (IC), capacitors, and connectors. 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+. By openly sharing this multi-scene benchmark dataset along with the baseline codes, we hope to foster transparent, traceable, and comparable developments of advanced data processing across various scientific communities, including, but not limited to, computer vision and remote sensing. Emphasizing our commitment to supporting a collaborative and inclusive scientific community, all materials, including code, data, ground truth, and masks, will be accessible at https://github.com/hifexplo/PCBVision.

翻译：针对电子废弃物（E-waste）回收这一关键议题，本文致力于开发先进的自动化数据处理流程，为决策制定和过程控制提供基础。为契合循环经济与联合国可持续发展目标的宏观愿景，本研究利用RGB和高光谱成像数据，采用非侵入式分析方法，从定量和定性两个维度深入解析电子废弃物流组成，以优化回收效率。本文介绍了“PCB-Vision”——一个开创性的RGB-高光谱印刷电路板（PCB）基准数据集，包含53幅高空间分辨率的RGB图像及其在可见光-近红外（VNIR）范围内对应的高光谱分辨率数据立方体。基于开放科学原则，该数据集通过提供高质量的真实标注（聚焦于三种主要PCB组件：集成电路（IC）、电容器和连接器），为研究人员提供了全面的资源。我们针对所提出的数据集进行了广泛的统计分析，并评估了多种最先进（SOTA）模型的性能，包括U-Net、Attention U-Net、Residual U-Net、LinkNet和DeepLabv3+。通过公开共享这一多场景基准数据集及其基线代码，我们希望促进跨计算机视觉与遥感等多个科学领域透明、可追溯且可比较的先进数据处理方法发展。所有材料（包括代码、数据、真实标注和掩膜）均可在https://github.com/hifexplo/PCBVision 获取，以此彰显我们对支持协作与包容性科学共同体的承诺。