New Benchmark for Household Garbage Image Recognition

TL;DR

This paper introduces a new comprehensive dataset, HGI-30, for household garbage image classification, addressing the lack of stable datasets and enabling improved deep learning methods in this domain.

Contribution

The creation of the HGI-30 dataset with 18,000 images across 30 classes, providing a new benchmark for research in household garbage image recognition.

Findings

Deep CNNs achieve baseline performance on HGI-30

HGI-30 covers diverse lighting, backgrounds, and angles

Dataset facilitates future research in robust garbage classification

Abstract

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.