  Automatic crack detection is a critical task that has the potential to
drastically reduce labor-intensive building and road inspections currently
being done manually. Recent studies in this field have significantly improved
the detection accuracy. However, the methods often heavily rely on costly
annotation processes. In addition, to handle a wide variety of target domains,
new batches of annotations are usually required for each new environment. This
makes the data annotation cost a significant bottleneck when deploying crack
detection systems in real life. To resolve this issue, we formulate the crack
detection problem as a weakly-supervised problem and propose a two-branched
framework. By combining predictions of a supervised model trained on low
quality annotations with predictions based on pixel brightness, our framework
is less affected by the annotation quality. Experimental results show that the
proposed framework retains high detection accuracy even when provided with low
quality annotations. Implementation of the proposed framework is publicly
available at https://github.com/hitachi-rd-cv/weakly-sup-crackdet.
