Performance Comparison of Pretrained Deep Learning Models for Landfill Waste Classification

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—VGG16, 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.