Design and Implementation of Garbage Classification System Based on Deep Learning

The rapid development of computer vision makes man-machine interaction possible and has a wide application prospect. At present, the Chinese government has implemented the garbage classification policy in various places, and garbage classification has been paid more and more attention by people. But it happens all the time that garbage is misclassified. This essay proposes an image recognition system to help people classify garbage, which can identify different kinds of garbage. The training data set used to train the system is made up of images taken by a camera. The image is preprocessed by rotation, cutting and other methods. Then ResNet50 is selected to train the preprocessed image. The experimental results show that the garbage classification system in this essay can classify garbage effectively and improve the accuracy of garbage classification.