A sight on defect detection methods for imbalanced industrial data

ITM Web of Conferences

Hamlich Mohamed¹  Chaabi Meryem¹ 

[1] CCPS Laboratory, ENSAM, University of Hassan II;

关键词: defect detection;    imbalanced data;    data augmentation;    one-class classification;   

DOI  :  10.1051/itmconf/20224301012

来源: DOAJ

【 摘 要 】

Product defect detection is a challenging task, especially in situations where is difficult and costly to collect defect samples. Which make it quite difficult to apply supervised algorithms as their performances decrease by training the model on imbalanced data. To tackle this problem, researchers used data augmentation and one-class classification to detect defects in industrial areas. In this paper, we list defect detection applications for imbalanced industrial data and we report the benefits and limitation of those methods.

【 授权许可】

Unknown