A waste classification method based on a capsule network

Abstract

Garbage recycling and automatic sorting are efficient ways to address the paradox of rising municipal waste. Although traditional image classification methods can solve the rubbish image classification problem, they ignore the spatial relationship between features, which can easily lead to misclassification of the same object. In this paper, we propose the ResMsCapsule network, which is a trash picture categorization model based on the capsule network. By combining the residual network and multi-scale module, the ResMsCapsule network can improve the performance of the basic capsule network greatly. Extensive experiments using the publicly available dataset TrashNet show that the ResMsCapsule method has a simpler network structure and higher garbage classification accuracy. The classification accuracy of the ResMsCapsule network is 91.41%, and the number of parameters is only 40% of that of ResNet18, which is better than other image classification algorithms.

Keywords: Capsule network; Feature fusion; Mixing modules; Residual network; Waste classification.