| Autor: |
Cathaoir Agnew, Dishant Mewada, Eoin M. Grua, Ciarán Eising, Patrick Denny, Mark Heffernan, Ken Tierney, Pepijn Van de Ven, Anthony Scanlan |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Intelligent Systems with Applications, Vol 18, Iss , Pp 200229- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2667-3053 |
| DOI: |
10.1016/j.iswa.2023.200229 |
| Popis: |
As the amount of waste being produced globally is increasing, there is a need for more efficient waste management solutions to accommodate this expansion. The first step in waste management is the collection of bins or containers. Each bin truck in a fleet is assigned a collection route. As the bin trucks have a finite amount of storage for waste, accepting overfilled bins may result in filling this storage before the end of the collection route. This creates inefficiencies as a second bin truck is needed to finish the collection route if the original becomes full. Currently, the recording and tracking of overfilled bins is a manual process, requiring the bin truck operator to undertake this task, resulting in longer collection route durations. To create a more efficient and automated process, computer vision methods are considered for the task of detecting the bin status. Video footage from a commercial collection route for two bin types, automated side loader (ASL) and front-end loader (FEL), was utilized to create appropriate computer vision datasets for the task of fully supervised object detection and instance segmentation. Selected state-of-the-art object detection and instance segmentation algorithms were used to investigate their performances on this proprietary dataset. A mean average precision (mAP) score of 0.8 or greater was achieved with each model, reflecting the effectiveness of using computer vision as a tool to automate the process of recording overfilled bins. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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