Single, S.; Iranmanesh, S.; Raad, R. RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep Learning. Information2023, 14, 633.
Single, S.; Iranmanesh, S.; Raad, R. RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep Learning. Information 2023, 14, 633.
Single, S.; Iranmanesh, S.; Raad, R. RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep Learning. Information2023, 14, 633.
Single, S.; Iranmanesh, S.; Raad, R. RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep Learning. Information 2023, 14, 633.
Abstract
The accurate classification of landfill waste diversion plays a critical role in efficient waste management practices. Traditional approaches, such as visual inspection, weighing and volume measurement, and manual sorting, have been widely used but suffer from subjectivity, scalability, and labour requirements. In contrast, machine learning approaches, particularly Convolutional Neural Networks (CNN), have emerged as powerful deep learning models for waste detection and classification. This paper analyses VGG-16, InceptionResNetV2, DenseNet121, Inception V3, and MobileNetV2 models to classify real-life waste when trained on pristine and unadulterated materials, versus samples collected at a landfill site. When training on DiversionNet, the unadulterated material dataset with labels required for landfill modelling, classification accuracy was limited to 49.69% in the real environment. Using real-world samples in the newly formed RealWaste dataset showed that practical applications for deep learning in waste classification is possible, with Inception V3 reaching 89.19% classification accuracy on the full spectrum of labels required for accurate modelling.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
