Research On Machine Learning Methods For Garbage Classification Data

Since 2019,China's new living garbage management regulations have been implemented.After that we must carefully classify the garbage before throwing it away.However,because the variety of living garbage and various garbage classification standards,it may be difficult for people to quickly and correctly classify the garbage.With the rapid development of artificial intelligence,machine learning combined with image recognition technology has been widely used in all aspects of life.Using algorithms to realized images can greatly improve the efficiency of garbage classification and bring convenience to people's life.This paper uses the garbage image data sets collecting from the Internet.At first,extracting GH descriptor,HOG descriptor and LBP descriptor of images based on traditional machine learning represented by support vector machine(SVM)and K-nearest neighbor(KNN),and uses these two algorithms to classify images,the highest accuracy has reached 79% and 85.1% respectively.Then,taking the convolutional neural network as an example,based on a classic model named VGGNet,this paper innovatively simplified VGGNet by reduce the number of convolution kernels and convolution layers,modify the size of convolution kernels to ensure that the model classification accuracy loss is low and the cost of running memory of the model is greatly reduced,which greatly improves the training efficiency of the model.the accuracy of Mini-VGG has reached 93%,so this method is valuable.It is can be concluded that the classification accuracy of traditional machine learning algorithm is basically the same as deep learning if the feature selection is appropriately.In the future,the field of image classification will still be dominated by deep learning,but the traditional machine learning algorithm will still has a place.