Few-Shot Learning for Industrial Defect Detection on Novel Scanning Electron Microscopy Datasets

NLDL 2026 Conference Submission34 Authors

Keywords: Industrial defect detection, convolutional neural networks, few-shot learning, scanning electron microscopy images.

Abstract: Industrial Defect Detection (IDD) involves identifying defects in different products through the analysis of manufacturing images. Over recent years convolutional neural networks (CNN) have become the preferred method to reliably solve this task, though a lack of labeled data has been a key challenge for supervised methods that rely on CNNs. Few-Shot Learning (FSL) offers a promising solution by enabling models to learn tasks from only a small number of labeled examples. However, it shifts the need for large labeled datasets to the pre-training stage, raising questions about how well these models generalize to new domains, such as different imaging modalities. Therefore, this study evaluates state-of-the-art FSL methods, trained on public optical datasets, for their effectiveness in IDD when tested on scanning electron microscopy (SEM) images. To facilitate benchmarking, this article also introduces three distinct SEM datasets for defect detection purposes. Through this assessment the study is able to identify strengths, challenges, and potential areas of improvement to motivate further research.

Serve As Reviewer: ~Raghav_Vacher1

Submission Number: 34