Convolutional Neural Network Based Technique for Efficient Waste Classification

Waste management has proved to be a significant environmental challenge due to the increasing global population and limited recycling efforts. This study proposes an approach using deep learning and computer vision techniques to automate waste sorting. By employing convolutional neural networks like ResNet50v2 and VGG16, along with Transfer Learning, a robust model capable of accurately categorizing waste for recycling and disposal has been developed. Through preprocessing and data augmentation techniques, we have tried to improve the performance of our models. Through our experiment, we have demonstrated that VGG16 and ResNet50 models are 93.45 and 93.02 percent accurate respectively. This research exemplifies the method for automated garbage sorting hence it significantly contributes to sustaining the environment by ultimately reducing pollution and carbon emissions.