  In this study, we consider the problem of detecting cracks from the image of
a concrete surface for automated inspection of infrastructure, such as bridges.
Its overall accuracy is determined by how accurately thin cracks with sub-pixel
widths can be detected. Our interest is in making it possible to detect cracks
close to the limit of thinness if it can be defined. Toward this end, we first
propose a method for training a CNN to make it detect cracks more accurately
than humans while training them on human-annotated labels. To achieve this
seemingly impossible goal, we intentionally lower the spatial resolution of
input images while maintaining that of their labels when training a CNN. This
makes it possible to annotate cracks that are too thin for humans to detect,
which we call super-human labels. We experimentally show that this makes it
possible to detect cracks from an image of one-third the resolution of images
used for annotation with about the same accuracy. We additionally propose three
methods for further improving the detection accuracy of thin cracks: i)
P-pooling to maintain small image structures during downsampling operations;
ii) Removal of short-segment cracks in a post-processing step utilizing a prior
of crack shapes learned using the VAE-GAN framework; iii) Modeling uncertainty
of the prediction to better handle hard labels beyond the limit of CNNs'
detection ability, which technically work as noisy labels. We experimentally
examine the effectiveness of these methods.
