  Defects increase the cost and duration of construction projects as they
require significant inspection and documentation efforts. Automating defect
detection could significantly reduce these efforts. This work focuses on
detecting honeycombs, a substantial defect in concrete structures that may
affect structural integrity. We compared honeycomb images scraped from the web
with images obtained from real construction inspections. We found that web
images do not capture the complete variance found in real-case scenarios and
that there is still a lack of data in this domain. Our dataset is therefore
freely available for further research. A Mask R-CNN and EfficientNet-B0 were
trained for honeycomb detection. The Mask R-CNN model allows detecting
honeycombs based on instance segmentation, whereas the EfficientNet-B0 model
allows a patch-based classification. Our experiments demonstrate that both
approaches are suitable for solving and automating honeycomb detection. In the
future, this solution can be incorporated into defect documentation systems.
