The invention discloses a garbage classification method based on a
hybrid convolutional neural network, and belongs to the technical field of garbage classification and
recovery. The method solves theproblems that an existing method is low in garbage classification precision and long in required
training time. According to a
hybrid convolutional neural network model, a convolutional layer, batchstandardization, a maximum
pooling layer and a full connection layer are flexibly applied, and BN batch
standardization is applied to each convolutional layer and each full connection layer, so that the
feature extraction capability of the model is further enhanced, the effect of each layer is brought into full play, and a relatively good
classification result is obtained. By utilizing the regularization effect of the BN layer, the maximum
pooling layer is properly added to perform statistics on the features, the
feature dimension is reduced, the representation capability is improved, fittingcan be well performed, the convergence speed is high, the parameter quantity is small, the calculation complexity is low, and the method has obvious advantages compared with a traditional
convolutional neural network. Meanwhile, an optimizer of SGDM + Nesterov is adopted in the model, and finally the classification accuracy of the model on the image reaches 92.6%. The method can be applied to household garbage classification.