A Garbage Classification and Environmental Impact Assessment Model Based on Image Recognition and Artificial Intelligence

With the rapid urbanization process, waste management has become a significant environmental issue globally.Waste sorting, as an effective method of resource recycling and environmental protection, has gradually become a key solution to the waste pollution problem.Traditional waste classification methods rely on manual labor, which is inefficient and prone to errors, making them inadequate for modern urban waste management.In recent years, image recognition and artificial intelligence (AI)-based methods for waste classification have gained widespread attention, with deep learning techniques, particularly Convolutional Neural Networks (CNNs), showing great potential in waste sorting.However, existing research on waste classification models faces challenges such as imperfect network structures, insufficient training data, and poor environmental adaptability, which limit their application in complex environments.This study proposes a waste classification model based on image recognition and AI to enhance classification accuracy and efficiency.First, an improved PCANet and SDenseNet network structure is combined to propose a new feature extraction and representation method, enhancing the model's feature learning ability.Secondly, a layered learning strategy, combined with the traditional backpropagation algorithm, is used to optimize the training process and improve learning efficiency.Finally, experimental results demonstrate that the proposed waste classification model significantly outperforms traditional models in classification accuracy and processing capability in various environments, providing a new solution for the advancement of waste classification technologies.