Office Garbage Intelligent Classification Based on Inception-v3 Transfer Learning Model

Abstract

With the increase in garbage production, the problem of garbage pollution is becoming more and more serious. Garbage recognition and classification can reduce the environmental burden, but there are still some challenges. Image classification, an image processing method that separates different categories of objects according to different characteristics reflected in the image information. This paper collected and produced an image data set with 2313 photos of different office garbage, and proposed an intelligent classification garbage can to solve the realistic problems. That method based on transfer techniques to retain the excellent feature extraction ability of the Inception-v3 model of TensorFlow, which can recognize objects through the convolutional neural network model. The experimental results showed that the garbage classification effect was obvious and the average accuracy rate reached 95.33%.