Research on deep learning image recognition technology in garbage classification

Abstract

Nowadays, with the rapid development of industry, people's daily life is becoming more and more rich and diverse. However, while enjoying the pleasure brought by material life, a large number of garbage are produced. They are various, and people lack the knowledge related to garbage classification, which leads to the difficulty of manual or robot classification. This paper studies a garbage classification algorithm model based on deep learning convolutional neural network Efficientnet to help identify garbage classification. In this research, data augmentation and normalization are carried out to solve the problem of small amount of data sets and different sizes of pictures. Efficientnet is used to extract the features of images. In order to solve the problem that BN has no obvious effect on small batches in the network, we replace BN with group normalization (GN). In order to prevent some irrelevant information in the image from affecting the training of the model, we add attention mechanism after the output of Efficientnet to emphasize or select the important information of the target processing object, and suppress some irrelevant details, so that the model can focus on the key features and better identify the image; according to the above process, we use softmax to classify the spam image and divide it into four categories (Recyclables, Kitchen garbage, Hazardous garbage, Other garbage) The results show that the model can effectively extract the features of the input garbage image, and get accurate judgment, and identify the types of garbage. The experimental results show that the average accuracy of the algorithm model is high, and has good classification performance and robustness. In the practical significance of the research, this reliable model can help people quickly know the type of garbage, or can be applied to robot sorting, to help detect the types of garbage for robot judgment and sorting, so it has very important application scenarios and significance.