  Recent studies on deep-learning-based small defection segmentation approaches
are trained in specific settings and tend to be limited by fixed context.
Throughout the training, the network inevitably learns the representation of
the background of the training data before figuring out the defection. They
underperform in the inference stage once the context changed and can only be
solved by training in every new setting. This eventually leads to the
limitation in practical robotic applications where contexts keep varying. To
cope with this, instead of training a network context by context and hoping it
to generalize, why not stop misleading it with any limited context and start
training it with pure simulation? In this paper, we propose the network SSDS
that learns a way of distinguishing small defections between two images
regardless of the context, so that the network can be trained once for all. A
small defection detection layer utilizing the pose sensitivity of phase
correlation between images is introduced and is followed by an outlier masking
layer. The network is trained on randomly generated simulated data with simple
shapes and is generalized across the real world. Finally, SSDS is validated on
real-world collected data and demonstrates the ability that even when trained
in cheap simulation, SSDS can still find small defections in the real world
showing the effectiveness and its potential for practical applications.
