RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep Learning
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 r...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
|
| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/14/12/633 |
| _version_ | 1797380637834870784 |
|---|---|
| author | Sam Single Saeid Iranmanesh Raad Raad |
| author_facet | Sam Single Saeid Iranmanesh Raad Raad |
| author_sort | Sam Single |
| collection | DOAJ |
| description | 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 are possible, with Inception V3 reaching 89.19% classification accuracy on the full spectrum of labels required for accurate modelling. |
| first_indexed | 2024-03-08T20:41:08Z |
| format | Article |
| id | doaj.art-b74b15323f914306884831abaef997ca |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-08T20:41:08Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-b74b15323f914306884831abaef997ca2023-12-22T14:15:50ZengMDPI AGInformation2078-24892023-11-01141263310.3390/info14120633RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep LearningSam Single0Saeid Iranmanesh1Raad Raad2School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, AustraliaSchool of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, AustraliaSchool of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, AustraliaThe 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 are possible, with Inception V3 reaching 89.19% classification accuracy on the full spectrum of labels required for accurate modelling.https://www.mdpi.com/2078-2489/14/12/633classificationmachine learningdeep learningconvolution neural networksdatasetlandfill waste |
| spellingShingle | Sam Single Saeid Iranmanesh Raad Raad RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep Learning Information classification machine learning deep learning convolution neural networks dataset landfill waste |
| title | RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep Learning |
| title_full | RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep Learning |
| title_fullStr | RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep Learning |
| title_full_unstemmed | RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep Learning |
| title_short | RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep Learning |
| title_sort | realwaste a novel real life data set for landfill waste classification using deep learning |
| topic | classification machine learning deep learning convolution neural networks dataset landfill waste |
| url | https://www.mdpi.com/2078-2489/14/12/633 |
| work_keys_str_mv | AT samsingle realwasteanovelreallifedatasetforlandfillwasteclassificationusingdeeplearning AT saeidiranmanesh realwasteanovelreallifedatasetforlandfillwasteclassificationusingdeeplearning AT raadraad realwasteanovelreallifedatasetforlandfillwasteclassificationusingdeeplearning |