Deep learning based garbage recognition system

The rapid increase in the amount of rubbish and the diversity of the substances in people's daily rubbish are two major problems of rubbish disposal at present, and the rubbish problem brings a serious burden to the environment as well as a waste of resources. At present, the main approach to deal with the waste problem is through waste separation. However, today's traditional manual classification method is still used to screen the rubbish, which is labour-intensive with low sorting efficiency. This paper proposes a deep learning-based intelligent rubbish classification system, which can quickly achieve the positioning and recognition of rubbish in complex backgrounds for classification by incoming pictures and effectively improve the efficiency of rubbish classification. The system uses TensorFlow as the deep learning platform, and the combination of a convolutional neural network and a fully connected neural network is used to achieve the construction of the deep learning model. Meanwhile, Some nodes of the fully connected layer are discarded with a given probability in the training phase to avoid the overfitting of the system to optimise the system, and the system is also optimised by collecting various types of rubbish pictures and building inspection datasets, using data augmentation to improve the robustness of the model, the fast and accurate identification of different types of rubbish is achieved. The experimental results demonstrate that the intelligent rubbish classification system proposed in this paper has a high performance in terms of both accuracy and efficiency.