Outdoor Garbage Classification Using Deep Learning Algorithm

Abstract

The process of classifying waste is a crucial part of garbage management since it makes it easier to identify the different categories of waste and how to handle them. The manual and labor-intensive nature of traditional garbage classification techniques can lead to mistakes and discrepancies. With the amount of waste produced worldwide rising, more accurate and efficient methods of sorting waste are needed. Automating waste categorization has shown promising results when machine learning techniques, including deep learning algorithms, are applied. Of these techniques, the VGG architecture has been used extensively for image classification applications and has achieved state-of-the-art performance on several benchmarks. Multiple convolutional layers, multiple pooling layers, and numerous fully linked layers make up the layers of the VGG architecture. This design is capable of learning complex. The CNN model is trained using a large dataset of trash photographs that are improved and pre-processed to improve the model's accuracy. To determine how effective the proposed method is at classifying smart waste, it is evaluated on a test dataset and compared with other state-of-the-art methods. The results demonstrate that the proposed method can accurately classify images of trash, improving waste management practices and reducing environmental contamination.