The invention discloses a low-overhead household garbage classification method based on a dense convolutional network, and the method comprises the steps: (1), preprocessing data, (2), building the dense convolutional network, (3), dividing a data set into a training set, a verification set and a test set, and (4), selecting a proper optimizer and a loss function in a training process, setting hyper-parameters, and setting an evaluation index as the accuracy rate. According to the method, optimization is carried out in preprocessing of input data, matrix fusion is carried out on a three-channel color image and an edge detection image to serve as input of a model, and feature information is enhanced; a dense convolutional network structure is constructed; a Dropout layer is additionally arranged; a learning rate self-adjusting and hyper-parameter adjusting method is used.According to the method, the model has enough feature extraction capability; the feature mapping of the model is usedas the input of a subsequent layer; the problem of gradient disappearance caused by a deep network is relieved; good balance is realized on the aspects of low overhead and high precision; and 90.8% of precision and 5.08 M of file size are realized.