Depth-guided volume estimation of trash bins in an uncontrolled environment | |
| Author | Rupakheti, Sagun |
| Call Number | AIT Thesis no.DSAI-24-08 |
| Subject(s) | Waste management--Data processing Deep learning (Machine learning) |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Data Science and Artificial Intelligence |
| Publisher | Asian Institute of Technology |
| Abstract | Efficient waste management is vital for reducing environmental and health hazards. However, current waste volume measurement methods lack precision and efficiency. This study proposes an innovative deep learning architecture to accurately estimate waste volume by detecting and segmenting trash cans from uncontrolled environment. Lever aging advanced computer vision and deep learning techniques, the research aims to de velop robust models to address occlusion challenges and provide accurate waste volume estimations. This approach holds promise for promoting sustainable waste management practices and mitigating environmental risks in various settings, contributing to a healthier and more sustainable living environment. |
| Year | 2024 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Data Science and Artificial Intelligence (DSAI) |
| Chairperson(s) | Mongkol Ekpanyapong |
| Examination Committee(s) | Thammarat Koottatep;Chaklam Silpasuwanchai;Gunasekara, Kavinda |
| Scholarship Donor(s) | AIT Scholarship |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2024 |