Recyclable waste image recognition based on deep learning

This study aims to improve the accuracy of waste sorting through deep learning and to provide a possibility for intelligent waste classification based on computer vision/mobile phone terminals. A classification model of recyclable waste images based on deep learning is proposed in this paper. In this waste classification model, the self-monitoring module is added to the residual network model, which can integrate the relevant features of all channel graphs, compress the spatial dimension features, and have a global receptive field. But the number of channels is still kept unchanged; thereby, the model can improve the representation ability of the feature map and can automatically extract the features of different types of waste images. The proposed model was tested on the TrashNet dataset to classify recyclable waste and compare its classification performance with other algorithms. Experimental results show that the image classification accuracy of this model reaches 95.87%.

Automated summary

This study introduces a waste classification model based on deep learning, which incorporates a self-monitoring module into the residual network model in order to enhance the representation capability of the feature map. The proposed model achieves a high accuracy in classifying recyclable waste images.