Waste Detection Based On Mask R-CNN
Yushuo Chang
Waste Detection Based On Mask R-CNN.
Rel. Bartolomeo Montrucchio, Antonio Costantino Marceddu. Politecnico di Torino, Corso di laurea magistrale in Communications And Computer Networks Engineering (Ingegneria Telematica E Delle Comunicazioni), 2022
|
Preview |
PDF (Tesi_di_laurea)
- Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (5MB) | Preview |
|
|
Archive (ZIP) (Documenti_allegati)
- Altro
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (46MB) |
Abstract
In recent years, with the rapid development of the world economy and the continuous improvement of people's consumption levels, a large amount of waste has been generated. The random discarding, simple stacking, and subsequent treatment of this waste are causing many problems. For example, it destroys the ecological environment, pollutes water, soil, and air, leads to a large number of mosquitoes and bacteria, and increases the probability of infectious diseases. The implementation of waste classification can effectively improve the living environment and reduce waste pollution to the environment, which is conducive to ensuring people's health and sustainable economic development. It can also help people classify and recycle waste more effectively, so it improves the efficiency of waste recycling.
Based on deep learning algorithms and object detection technology, this thesis implements the detection and classification of waste and focuses on the following three goals: 1) Use VIA to perform annotation and classification
Tipo di pubblicazione
URI
![]() |
Modifica (riservato agli operatori) |
