A Study on Data Augmentation Techniques for Visual Defect Detection in Manufacturing

Abstract

AbstractDeep learning-based defect detection is rapidly gaining importance for automating visual quality control tasks in industrial applications. However, due to usually low rejection rates in manufacturing processes, industrial defect detection datasets are inherent to three severe data challenges: data sparsity, data imbalance, and data shift. Because the acquisition of defect data is highly cost″​=intensive, and Deep Learning (DL) algorithms require a sufficiently large amount of data, we are investigating how to solve these challenges using data oversampling and data augmentation (DA) techniques. Given the problem of binary defect detection, we present a novel experimental procedure for analyzing the impact of different DA-techniques. Accordingly, pre-selected DA-techniques are used to generate experiments across multiple datasets and DL models. For each defect detection use-case, we configure a set of random DA-pipelines to generate datasets of different characteristics. To investigate the impact of DA-techniques on defect detection performance, we then train convolutional neural networks with two different but fixed architectures and hyperparameter sets. To quantify and evaluate the generalizability, we compute the distances between dataset derivatives to determine the degree of domain shift. The results show that we can precisely analyze the influences of individual DA-methods, thus laying the foundation for establishing a mapping between dataset properties and DA-induced performance enhancement aiming for enhancing DL development. We show that there is no one-fits all solution, but that within the categories of geometrical and color augmentations, certain DA-methods outperform others.