  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.
