  Pixel-level crack segmentation is widely studied due to its high impact on
building and road inspections. While recent studies have made significant
improvements in accuracy, they typically heavily depend on pixel-level crack
annotations, which are time-consuming to obtain. In earlier work, we proposed
to reduce the annotation cost bottleneck by reformulating the crack
segmentation problem as a weakly-supervised problem -- i.e. the annotation
process is expedited by sacrificing the annotation quality. The loss in
annotation quality was remedied by refining the inference with per-pixel
brightness values, which was effective when the pixel brightness distribution
between cracks and non-cracks are well separated, but struggled greatly for
lighter-colored cracks as well as non-crack targets in which the brightness
distribution is less articulated. In this work, we propose an annotation
refinement approach which takes advantage of the fact that the regions falsely
annotated as cracks have similar local visual features as the background.
Because the proposed approach is data-driven, it is effective regardless of a
dataset's pixel brightness profile. The proposed method is evaluated on three
crack segmentation datasets as well as one blood vessel segmentation dataset to
test for domain robustness, and the results show that it speeds up the
annotation process by factors of 10 to 30, while the detection accuracy stays
at a comparable level.
