PublicGarbageNet : A Deep Learning Framework for Public Garbage Classification

Intelligent garbage classification is an important technique for garbage harmless, reducing and recycling treatment. In this paper, we propose a public garbage classification algorithm based on the CNN architecture, namely PublicGarbageNet. The proposed algorithm is a multi-task classification algorithm in which one task identifies four major categories of domestic garbage, and the other task achieves recognition of 10 subclasses garbage. The two classification tasks are related to each other, and the joint loss function is helpful to improve the accuracy of garbage recognition. Considering the fact that the existing garbage datasets are incomplete in classes and small in quantity, we constructed a new pubic garbage dataset including 10 subclasses and a total of 10624 images. In order to obtain better performance, systematically studies such as backbone optimization selection, data augmentation, learning rate optimization, and label smoothing have been made, and finally the accuracy of the optimized model reaches 96.35%.