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

Viewed

Total article views: 522 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
435 68 19 522 17 20
  • HTML: 435
  • PDF: 68
  • XML: 19
  • Total: 522
  • BibTeX: 17
  • EndNote: 20
Views and downloads (calculated since 04 Apr 2024)
Cumulative views and downloads (calculated since 04 Apr 2024)

Viewed (geographical distribution)

Total article views: 491 (including HTML, PDF, and XML) Thereof 491 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 21 Jan 2025
Download
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.