Design of a garbage classification system based on deep transfer learning

In light of the present challenges related to the insufficient autonomous identification and classification of garbage, along with the management and movement of garbage bins, a novel intelligent garbage classification system utilizing deep learning is suggested. This system is designed to autonomously identify types of garbage and to finalize the mobile garbage classification system for packaging. An analysis of the three-dimensional structure of the garbage bin and a dynamic examination of the motion system of the three-wheeled chassis are performed. The Raspberry Pi 3B and STM32 microcontrollers are employed for management, while the Inception v3 network structure algorithm is carefully adjusted and enhanced to facilitate garbage image recognition and classification. The outcomes of experimental research demonstrate that the fine-tuned and optimized Inception v3 network structure algorithm effectively facilitates the identification of garbage image recognition, and the model demonstrates superior performance in recognizing and categorizing images. This investigation offers a reliable approach for enhancing the efficiency and accuracy of garbage classification, thereby contributing to environmental protection and sustainable development.