Garbage image classification method based on improved convolution neural network and long short-term memory network

Abstract

When dealing with garbage image classification task, with the deepening of network layer, convolutional neural network will lead to gradient disappearance / explosion and large time consumption. Therefore, a garbage image classification method combining improved convolutional neural network and long short-term memory network is proposed. Taking ResNet-50 as the network backbone, it is optimized by using deep separable convolution and attention mechanism. At the same time, supplemented by LSTM, the features extracted by convolution network and cyclic network are fused to complete classification output. Ablation experiments are carried out on this model and compared with other typical convolutional neural networks. The results show that the accuracy of this model increases by an average of 4.5%. The introduction of deep separable convolution can reduce the training time by about 35.4% compared with the baseline method.