Image-based Detection of Surface Defects in Concrete during Construction
This work addresses a domain-specific problem for construction inspection by automating defect detection, though it is incremental as it applies existing methods to a new dataset.
The paper tackled detecting honeycomb defects in concrete structures using image-based methods, finding that web-scraped images lack real-world variance and releasing a dataset, with Mask R-CNN and EfficientNet-B0 models showing suitability for automation.
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.