With the accelerated implementation of the garbage classification regulation in China, many applications for garbage classification based on virtual/augmented reality technologies have sprung up. Under the influence of the identification equipment platform and residents’ habits of using applications, there remain a number of shortcomings in convenience and practicability for this kind of application. A waste classification application scheme was proposed based on a lightweight neural network combined with mobile augmented reality and visualization technology. Firstly, the variable expansion convolution VD-MobileNet model method was proposed for garbage classification based on deep learning, which can solve the problems of limited computing capacity and a huge network of mobile devices. The receptive field was increased by introducing dilated convolution in the MobileNet model. The characteristic information of garbage could be expanded to enhance classification accuracy, and LeakyReLU activation function was

introduced to optimize the expression ability of the network. Secondly, the model was equipped with the WebAR technology, and a lightweight garbage classification information visualization system was designed for mobile devices. This system could operate cross different platforms, realize the diversified visual presentation of classified information, and enable flexible interactions. Experiments and evaluations show that the VD-MobileNet model could achieve excellent classification in the garbage classification data set and can effectively reduce the amount of calculation with constant parameters. In addition, the WebAR application system designed based on the model can provide users with reasonable and effective assistance in garbage disposal.

garbage classification, web-based augmented reality, MobileNet model, visualization technology, dilated convolution, WebAR