EWasteNet：一种用于电子废弃物分类的双流数据高效图像Transformer方法
EWasteNet: A Two-Stream Data Efficient Image Transformer Approach for E-Waste Classification
AI总结：
本文提出电子废弃物图像数据集E-Waste Vision Dataset及基于DeiT的双流分类框架EWasteNet，通过Sobel算子与ASPP注意力模块提取多尺度特征，在决策层融合实现96%分类准确率，助力电子废弃物高效回收。
摘要：
电子废弃物的不当处理在全球范围内带来了环境和健康风险，引发了严重关切。准确分类电子废弃物图像对于高效管理和回收至关重要。本文提出了一个全面的数据集，包含八种不同类别的电子设备图像，命名为E-Waste Vision Dataset。我们还提出了EWasteNet，这是一种基于数据高效图像Transformer（DeiT）的新型双流方法，用于精确分类电子废弃物图像。EWasteNet的第一流通过Sobel算子检测边缘，第二流通过Atrous Spatial Pyramid Pooling和注意力块捕获多尺度上下文信息。我们同时训练这两个流，并在决策层融合它们的特征。DeiT作为两个流的主干网络。对电子废弃物数据集的广泛分析表明了该方法的有效性，在电子废弃物分类中达到了96%的准确率。所提方法在应对全球电子废弃物管理问题方面展现出显著价值。它通过准确分类电子废弃物图像，促进了高效的废弃物管理和回收，减少了不当处理带来的健康与安全隐患。
英文摘要：
Improper disposal of e-waste poses global environmental and health risks, raising serious concerns. The accurate classification of e-waste images is critical for efficient management and recycling. In this paper, we have presented a comprehensive dataset comprised of eight different classes of images of electronic devices named the E-Waste Vision Dataset. We have also presented EWasteNet, a novel two-stream approach for precise e-waste image classification based on a data-efficient image transformer (DeiT). The first stream of EWasteNet passes through a sobel operator that detects the edges while the second stream is directed through an Atrous Spatial Pyramid Pooling and attention block where multi-scale contextual information is captured. We train both of the streams simultaneously and their features are merged at the decision level. The DeiT is used as the backbone of both streams. Extensive analysis of the e-waste dataset indicates the usefulness of our method, providing 96% accuracy in e-waste classification. The proposed approach demonstrates significant usefulness in addressing the global concern of e-waste management. It facilitates efficient waste management and recycling by accurately classifying e-waste images, reducing health and safety hazards associated with improper disposal.
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