Designing a Hybrid Neural System to Learn Real-world Crack Segmentation
  from Fractal-based Simulation
Achref Jaziri Martin Mundt Andres Fernandez Rodriguez Visvanathan Ramesh
Identification of cracks is essential to assess the structural integrity of
concrete infrastructure. However, robust crack segmentation remains a
challenging task for computer vision systems due to the diverse appearance of
concrete surfaces, variable lighting and weather conditions, and the
overlapping of different defects. In particular recent data-driven methods
struggle with the limited availability of data, the fine-grained and
time-consuming nature of crack annotation, and face subsequent difficulty in
generalizing to out-of-distribution samples. In this work, we move past these
challenges in a two-fold way. We introduce a high-fidelity crack graphics
simulator based on fractals and a corresponding fully-annotated crack dataset.
We then complement the latter with a system that learns generalizable
representations from simulation, by leveraging both a pointwise mutual
information estimate along with adaptive instance normalization as inductive
biases. Finally, we empirically highlight how different design choices are
symbiotic in bridging the simulation to real gap, and ultimately demonstrate
that our introduced system can effectively handle real-world crack
segmentation.