Industrial defect detection on the edge with deep learning over scarcely labeled and extremely imbalanced data

Abstract—Reliable automated defect detection is an integral part of modern manufacturing and improved performance can provide a competitive advantage. Despite the proven capabilities
of convolutional neural networks (CNNs) for image classification, application on real world tasks remains challenging due to the high demand for labeled and well balanced data of the common
supervised learning scheme. Semi-supervised learning (SSL) promises to achieve comparable accuracy while only requiring a small fraction of the training samples to be labeled. However, SSL
methods struggle with data imbalance and existing benchmarks do not reflect the challenges of real world applications. In this work we present a CNN-based defect detection unit for
thermal sensors. We describe how to collect data from a running process and release our dataset of 1k labeled and 293k unlabeled samples. Furthermore, we investigate the use of SSL under this
challenging real world task.