  Household garbage images are usually faced with complex backgrounds, variable
illuminations, diverse angles, and changeable shapes, which bring a great
difficulty in garbage image classification. Due to the ability to discover
problem-specific features, deep learning and especially convolutional neural
networks (CNNs) have been successfully and widely used for image representation
learning. However, available and stable household garbage datasets are
insufficient, which seriously limits the development of research and
application. Besides, the state of the art in the field of garbage image
classification is not entirely clear. To solve this problem, in this study, we
built a new open benchmark dataset for household garbage image classification
by simulating different lightings, backgrounds, angles, and shapes. This
dataset is named 30 Classes of Household Garbage Images (HGI-30), which
contains 18,000 images of 30 household garbage classes. The publicly available
HGI-30 dataset allows researchers to develop accurate and robust methods for
household garbage recognition. We also conducted experiments and performance
analysis of the state-of-the-art deep CNN methods on HGI-30, which serves as
baseline results on this benchmark.
