  The generalisation performance of a convolutional neural network (CNN) is
influenced by the quantity, quality, and variety of the training images.
Training images must be annotated, and this is time consuming and expensive.
The goal of our work was to reduce the number of annotated images needed to
train a CNN while maintaining its performance. We hypothesised that the
performance of a CNN can be improved faster by ensuring that the set of
training images contains a large fraction of hard-to-classify images. The
objective of our study was to test this hypothesis with an active learning
method that can automatically select the hard-to-classify images. We developed
an active learning method for Mask Region-based CNN (Mask R-CNN) and named this
method MaskAL. MaskAL involved the iterative training of Mask R-CNN, after
which the trained model was used to select a set of unlabelled images about
which the model was most uncertain. The selected images were then annotated and
used to retrain Mask R-CNN, and this was repeated for a number of sampling
iterations. In our study, MaskAL was compared to a random sampling method on a
broccoli dataset with five visually similar classes. MaskAL performed
significantly better than the random sampling. In addition, MaskAL had the same
performance after sampling 900 images as the random sampling had after 2300
images. Compared to a Mask R-CNN model that was trained on the entire training
set (14,000 images), MaskAL achieved 93.9% of that model's performance with
17.9% of its training data. The random sampling achieved 81.9% of that model's
performance with 16.4% of its training data. We conclude that by using MaskAL,
the annotation effort can be reduced for training Mask R-CNN on a broccoli
dataset with visually similar classes. Our software is available on
https://github.com/pieterblok/maskal.
