  Active learning aims to select the minimum amount of data to train a model
that performs similarly to a model trained with the entire dataset. We study
the potential of active learning for image segmentation in underwater
infrastructure inspection tasks, where large amounts of data are typically
collected. The pipeline inspection images are usually semantically repetitive
but with great variations in quality. We use mutual information as the
acquisition function, calculated using Monte Carlo dropout. To assess the
effectiveness of the framework, DenseNet and HyperSeg are trained with the
CamVid dataset using active learning. In addition, HyperSeg is trained with a
pipeline inspection dataset of over 50,000 images. For the pipeline dataset,
HyperSeg with active learning achieved 67.5% meanIoU using 12.5% of the data,
and 61.4% with the same amount of randomly selected images. This shows that
using active learning for segmentation models in underwater inspection tasks
can lower the cost significantly.
