Abstract: Aiming at the problems of complex structure and poor real-time performance of traditional garbage image classification models, a fast garbage images classification model based on depthwise separable convolution combined with a multi-channel attention mechanism was proposed. Firstly, depthwise separable convolution is constructed by using deep convolution and pointwise convolution, and the training time overhead of the mode is reduced by reducing the number of convolution parameters. Then the multi-channel attention mechanism is introduced to make the model pay more attention to the features with strong classification ability. Finally, the test is performed on open-source garbage image classification data sets such as TrashNet、Garbage-classify, and GINI. The experimental results show that the model has a lower time overhead than the current mainstream garbage image classification models while maintaining a higher recognition accuracy.

Key words: garbage images classification, deep convolution, pointwise convolution, multi-channel attention