Active learning using weakly supervised signals for quality inspection

Because manufacturing processes evolve fast and production visual aspect can vary significantly on a daily basis, the ability to rapidly update machine vision based inspection systems is paramount. Unfortunately, supervised learning of convolutional neural networks requires a significant amount of annotated images in order to learn effectively from new data. Acknowledging the abundance of continuously generated images coming from the production line and the cost of their annotation, we demonstrate it is possible to prioritize and accelerate the annotation process. In this work, we develop a methodology for learning actively,(1) from rapidly mined, weakly (i.e. partially) annotated data, enabling a fast, direct feedback from the operators on the production line and tackling a big machine vision weakness: false positives. These may arise with covariate shift, which happens inevitably due to changing conditions of the data acquisition setup. In that regard, we show domain-adversarial training(2) to be an efficient way to address this issue.