Articles | Volume 13, issue 1
https://doi.org/10.5194/jsss-13-63-2024
https://doi.org/10.5194/jsss-13-63-2024
Regular research article
 | 
04 Apr 2024
Regular research article |  | 04 Apr 2024

Cutout as augmentation in contrastive learning for detecting burn marks in plastic granules

Muen Jin and Michael Heizmann

Data sets

Synthetic dataset of plastic granules Muen Jin https://drive.google.com/drive/folders/190ywN1Yi-C18Nmji0ZoOmZuyKtToF8zy

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Short summary
Our research introduces an innovative method to improve the quality control of plastic granules, crucial in industries like manufacturing and automotive. We addressed a common issue: identifying "burn marks" on granules caused by overheating during processing. By combining advanced machine learning and a novel data augmentation technique called "cutout", we significantly enhanced the detection accuracy of these defects.