Data Augmentation Using Background Replacement for Automated Sorting of Littered Waste

The introduction of sophisticated waste treatment plants is making the process of trash sorting and recycling more and more effective and eco-friendly. Studies on Automated Waste Sorting (AWS) are greatly contributing to making the whole recycling process more efficient. However, a relevant issue, w...

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Bibliographic Details
Published in:Journal of imaging 2021-08, Vol.7 (8), p.144
Main Authors: Patrizi, Arianna, Gambosi, Giorgio, Zanzotto, Fabio Massimo
Format: Article
Language:English
Subjects:
automated waste sorting
Automation
background replacement
Classification
computer vision
convolutional neural networks
COVID-19
Data augmentation
Datasets
deep learning
Neural networks
Recycling
Robots
Waste treatment
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Summary:The introduction of sophisticated waste treatment plants is making the process of trash sorting and recycling more and more effective and eco-friendly. Studies on Automated Waste Sorting (AWS) are greatly contributing to making the whole recycling process more efficient. However, a relevant issue, which remains unsolved, is how to deal with the large amount of waste that is littered in the environment instead of being collected properly. In this paper, we introduce BackRep: a method for building waste recognizers that can be used for identifying and sorting littered waste directly where it is found. BackRep consists of a data-augmentation procedure, which expands existing datasets by cropping solid waste in images taken on a uniform (white) background and superimposing it on more realistic backgrounds. For our purpose, realistic backgrounds are those representing places where solid waste is usually littered. To experiment with our data-augmentation procedure, we produced a new dataset in realistic settings. We observed that waste recognizers trained on augmented data actually outperform those trained on existing datasets. Hence, our data-augmentation procedure seems a viable approach to support the development of waste recognizers for urban and wild environments.
ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging7080144