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.

本文提出了一个包含八种不同类别电子设备图像的全面数据集 ——E-Waste Vision Dataset，并介绍了 EWasteNet，一种基于数据有效的图像转换器（DeiT）的精确电子废弃物图像分类的新型两流方法。该方法通过 Sobel 算子检测边缘来完成第一流，通过 Atrous 空间金字塔池化和注意力块来捕捉多尺度上下文信息的第二流。我们同时训练这两个流，它们的特征在决策层被合并。该方法在处理电子废弃物管理的全球关切方面具有显著的有用性，通过准确地对电子废弃物图像进行分类，减少了与不当处理相关的健康与安全风险，促进了高效的废物处理和回收。