  Dataset creation is typically one of the first steps when applying Artificial
Intelligence methods to a new task; and the real world performance of models
hinges on the quality and quantity of data available. Producing an image
dataset for semantic segmentation is resource intensive, particularly for
specialist subjects where class segmentation is not able to be effectively
farmed out. The benefit of producing a large, but poorly labelled, dataset
versus a small, expertly segmented dataset for semantic segmentation is an open
question. Here we show that a large, noisy dataset outperforms a small,
expertly segmented dataset for training a Fully Convolutional Network model for
semantic segmentation of corrosion in images. A large dataset of 250 images
with segmentations labelled by undergraduates and a second dataset of just 10
images, with segmentations labelled by subject matter experts were produced.
The mean Intersection over Union and micro F-score metrics were compared after
training for 50,000 epochs. This work is illustrative for researchers setting
out to develop deep learning models for detection and location of specialist
features.
