Szczegóły publikacji
Opis bibliograficzny
Real time multipurpose smart waste classification model for efficient recycling in smart cities using multilayer convolutional neural network and perceptron / Ali Usman Gondal, Muhammad Imran Sadiq, Tariq Ali, Muhammad Irfan, Ahmad Shaf, Muhammad Aamir, Muhammad Shoaib, Adam GŁOWACZ, Ryszard TADEUSIEWICZ, Eliasz KAŃTOCH // Sensors [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1424-8220. — 2021 — vol. 21 iss. 14 art. no. 4916, s. 1-15. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 14–15, Abstr. — Publikacja dostępna online od: 2021-07-19
Autorzy (10)
- Gondal Ali Usman
- Sadiq Muhammad Imran
- Ali Tariq
- Irfan Muhammad
- Shaf Ahmad
- Aamir Muhammad
- Shoaib Muhammad
- AGHGłowacz Adam
- AGHTadeusiewicz Ryszard
- AGHKańtoch Eliasz
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 135314 |
|---|---|
| Data dodania do BaDAP | 2021-07-21 |
| Tekst źródłowy | URL |
| DOI | 10.3390/s21144916 |
| Rok publikacji | 2021 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Sensors |
Abstract
Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.