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• W4313361235 abstract "Polyethylene terephthalate (PET) bottle recycling is a highly automated task; however, manual quality control is required due to inefficiencies of the process. In this paper, we explore automation of the quality control sub-task, namely visual bottle detection, using convolutional neural network (CNN)-based methods and synthetic generation of labelled training data. We propose a synthetic generation pipeline tailored for transparent and crushed PET bottle detection; however, it can also be applied to undeformed bottles if the viewpoint is set from above. We conduct various experiments on CNNs to compare the quality of real and synthetic data, show that synthetic data can reduce the amount of real data required and experiment with the combination of both datasets in multiple ways to obtain the best performance." @default.
• W4313361235 created "2023-01-06" @default.
• W4313361235 creator A5011374690 @default.
• W4313361235 creator A5054621323 @default.
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• W4313361235 date "2022-12-29" @default.
• W4313361235 modified "2023-09-25" @default.
• W4313361235 title "Synthetic Data Generation for Visual Detection of Flattened PET Bottles" @default.
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• W4313361235 doi "https://doi.org/10.3390/make5010002" @default.
• W4313361235 hasPublicationYear "2022" @default.
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