Performance Comparison of Pretrained Deep Learning Models for Landfill Waste Classification

Abstract

The escalating challenge of waste management, particularly in developed nations, necessitates innovative approaches to enhance recycling and sorting efficiency. This study investigates the application of Convolutional Neural Networks (CNNs) for landfill waste classification, addressing the limitations of traditional sorting methods. We conducted a performance comparison of five prevalent CNN models—VGG-16, InceptionResNetV2, DenseNet121, Inception V3, and MobileNetV2—using the newly introduced "RealWaste" dataset, comprising 4,752 labeled images. Our findings reveal that EfficientNet achieved the highest average testing accuracy of 96.31%, significantly outperforming other models. The analysis also highlighted common challenges in accurately distinguishing between metal and plastic waste categories across all models. This research underscores the potential of deep learning techniques in automating waste classification processes, thereby contributing to more effective waste management strategies and promoting environmental sustainability.