Design of Intelligent Garbage Classification System Based on GoogLeNet Convolutional Neural Network

In view of the problems of existing garbage classification methods, such as tedious sorting, low efficiency and supervision difficulties, an intelligent garbage classification system based on the GoogLeNet convolutional neural network is designed to overcome the shortcomings of market products concerning automatic identification and disposal, simple operation and suitability for household use. Based on GoogLeNet convolutional neural network, a four-category recognition classification algorithm based on garbage classification is designed. The collected garbage data set is fine-tuned and trained to achieve effective recognition of garbage types. This system employs a Raspberry PI as the core controller, combined with a CSI camera to obtain images and equipped with image recognition algorithm to achieve garbage classification. An independently designed mechanical structure and control unit, integrating classification information, enables automatic garbage disposal. The test analysis shows that the system can accurately perform garbage type identification and disposal, thereby effectively enhancing the convenience and efficiency of garbage classification. This holds great significance for the practical implementation of garbage classification at the user level.