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240926s2024 xx |||||o 00| ||eng c |
| 024 |
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|a 10.1016/j.wasman.2024.09.018
|2 doi
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2 |
|a pubmed24n1611.xml
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|a eng
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| 100 |
1 |
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|a Sirimewan, Diani
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Optimizing waste handling with interactive AI
|b Prompt-guided segmentation of construction and demolition waste using computer vision
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| 264 |
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|c 2024
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| 336 |
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 23.11.2024
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|a Date Revised 23.11.2024
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.
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|a Optimized and automated methods for handling construction and demolition waste (CDW) are crucial for improving the resource recovery process in waste management. Automated waste recognition is a critical step in this process, and it relies on robust image segmentation techniques. Prompt-guided segmentation methods provide promising results for specific user needs in image recognition. However, the current state-of-the-art segmentation methods trained for generic images perform unsatisfactorily on CDW recognition tasks, indicating a domain gap. To address this gap, a user-guided segmentation pipeline is developed in this study that leverages prompts such as bounding boxes, points, and text to segment CDW in cluttered environments. The adopted approach achieves a class-wise performance of around 70 % in several waste categories, surpassing the state-of-the-art algorithms by 9 % on average. This method allows users to create accurate segmentations by drawing a bounding box, clicking, or providing a text prompt, minimizing the time spent on detailed annotations. Integrating this human-machine system as a user-friendly interface into material recovery facilities enhances the monitoring and processing of waste, leading to better resource recovery outcomes in waste management
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| 650 |
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4 |
|a Journal Article
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| 650 |
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4 |
|a Automated waste recognition
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| 650 |
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4 |
|a Automation
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| 650 |
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4 |
|a Computer vision
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| 650 |
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4 |
|a Construction and demolition waste
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| 650 |
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4 |
|a Prompt-guided segmentation
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| 650 |
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4 |
|a Waste monitoring and sorting
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| 700 |
1 |
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|a Kunananthaseelan, Nilakshan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Raman, Sudharshan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Garcia, Reyes
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Arashpour, Mehrdad
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Waste management (New York, N.Y.)
|d 1999
|g 190(2024) vom: 15. Nov., Seite 149-160
|w (DE-627)NLM098197061
|x 1879-2456
|7 nnns
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| 773 |
1 |
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|g volume:190
|g year:2024
|g day:15
|g month:11
|g pages:149-160
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| 856 |
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|u http://dx.doi.org/10.1016/j.wasman.2024.09.018
|3 Volltext
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