3 research outputs found
Image-based Detection of Surface Defects in Concrete during Construction
Defects increase the cost and duration of construction projects. Automating
defect detection would reduce documentation efforts that are necessary to
decrease the risk of defects delaying construction projects. Since concrete is
a widely used construction material, this work focuses on detecting honeycombs,
a substantial defect in concrete structures that may even affect structural
integrity. First, images were compared that were either scraped from the web or
obtained from actual practice. The results demonstrate that web images
represent just a selection of honeycombs and do not capture the complete
variance. Second, Mask R-CNN and EfficientNet-B0 were trained for honeycomb
detection to evaluate instance segmentation and patch-based classification,
respectively achieving 47.7% precision and 34.2% recall as well as 68.5%
precision and 55.7% recall. Although the performance of those models is not
sufficient for completely automated defect detection, the models could be used
for active learning integrated into defect documentation systems. In
conclusion, CNNs can assist detecting honeycombs in concrete