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Enhancing Weakly Supervised Defect Detection Through Anomaly-Informed Weighted Training
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DOI：10.1109/TIM.2024.3476572.png)
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In numerous real-world situations, acquiring extensive sets of labeled data poses a formidable challenge. This study introduces an innovative approach for enhancing weakly supervised learning (WSL) through anomaly-informed weighted training (WT). The method not only is tested in diverse benchmark datasets such as CIFAR-10 and Fashion-MNIST by simulating a binary classification problem with only prior knowledge of some samples belonging to one class but also is applied to a real-world scenario which specifically aims to detect surface defects, like pitting, on ball screw surfaces. The proposed method leverages anomaly detection techniques to refine the training processes in a WSL setting, thereby effectively addressing the challenge of limited labeled data availability. The results demonstrated significant enhancements in the classification metrics, showing the potential of this method for industrial applications.
Keyword:
Training
Manufacturing
Labeling
Supervised learning
Feature extraction
Defect detection
Data models
Image coding
Encoding
Benchmark testing
Anomaly-informed learning
defect detection
positive and unlabeled (PU)-learning
weakly supervised learning (WSL)
weighted sample training
期刊
IF:
5.9
论文数:
1.9W
被引数:
5.8W
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