Application of residual network in household waste classification

With the acceleration of urbanization, the amount of garbage has skyrocketed. This makes the burden of sorting waste by manual means heavier and heavier, resulting in a large amount of waste accumulation. In this context, the classification of household garbage gradually received attention. In order to improve the efficiency of garbage classification, we should deal with the increasingly complicated garbage classification work and make garbage classification intelligent and efficient. In this paper, convolutional neural network intelligent algorithm is used to solve the classification problem of organic and inorganic garbage in daily life. The accuracy can reach 90.34% only with low sequence training. Compared with existing commonly used garbage classification and recognition algorithms, the proposed garbage classification and recognition algorithm has better recognition performance and is more suitable for widespread application.