| Title |
An Intelligent Scanning Vehicle for Waste Collection Monitoring |
| ID_Doc |
25794 |
| Authors |
Waltner, G; Jaschik, M; Rinnhofer, A; Possegger, H; Bischof, H |
| Title |
An Intelligent Scanning Vehicle for Waste Collection Monitoring |
| Year |
2022 |
| Published |
|
| DOI |
10.1007/978-3-031-06427-2_4 |
| Abstract |
While many industries have adopted digital solutions to improve ecological footprints and optimize services, new technologies have not yet found broad acceptance in waste management. In addition, past efforts to motivate households to improve waste separation have shown limited success. To reduce greenhouse gas emissions as part of a greater plan for fighting climate change, institutions like the European Union (EU) undertake strong efforts. In this context, developing intelligent digital technologies for waste management helps to increase the recycling rate and as a consequence reduces greenhouse gas emissions. Within this work, we propose an innovative computer vision system that is able to assess the residential waste in real-time and deliver individual feedback to the households and waste management companies with the aim of increasing recycling rates and thus reducing emissions. It consists of two core components: A compact scanning hardware designed specifically for rugged environments like the innards of a garbage truck and an intelligent software that applies a convolutional neural network (CNN) to automatically identify the composition of the waste which was dumped into the truck and subsequently delivers the results to a web portal for further analysis and communication. We show that our system can impact household separation behavior and result in higher recycling rates leading to noticeable reduction of CO2 emissions in the long term. |
| Author Keywords |
Convolutional neural networks; Deep learning; Computer vision; Cloud computing; Circular economy |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000870304100004 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Information Systems; Imaging Science & Photographic Technology |
| Research Area |
Computer Science; Imaging Science & Photographic Technology |
| PDF |
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