The study provides the deep learning-based garbage classification system the opportunity to adapt to the varying garbage classes. The proposed model embeds a prototype enhancement-based incremental evolution learning technique for garbage classification. The proposed IEL technique employs prototype enhancement to accurately and effectively represent data from existing classes. The model's ability to generalize is enhanced by the developed contrastive feature approach. This research offers a practical solution for the automatic everchanging garbage classification issue.

Deep neural network (DNN) based on incremental learning provides support for efficient garbage classification tasks. However, it is always challenging to accurately learn and preserve the information of known classes for updating DNN while new tasks are continuously emerging, which also affects the generalization performance of the model. To solve these issues, an incremental evolution learning (IEL) method based on prototype enhancement is proposed to accurately preserve data and improve the model generalization ability. First, a prototype enhancement method based on multidimensional Gaussian kernel density estimation is designed, which extends the prototype of each sample based on high-dimensional nonlinear data distribution. Then, the prototype enhancement accurately represents the known class data. Second, a contrastive feature method is proposed to constrain the consistency of features between tasks, which reduces the deviation between different tasks. Then, the extraction prefere...