Developing a Free and Open-source Automated Building Exterior Crack Inspection Software for Construction and Facility Managers
This addresses the time-consuming, inconsistent, and dangerous manual crack inspection process for construction and facility managers, though it is incremental as it applies existing AI methods to a new domain.
The study tackled the problem of automating building exterior crack inspection by developing ABECIS, a free and open-source software that uses segmentation algorithms, achieving median IoU scores of up to 0.958 for outdoor drone images and showing significant improvement with human verification.
Inspection of cracks is an important process for properly monitoring and maintaining a building. However, manual crack inspection is time-consuming, inconsistent, and dangerous (e.g., in tall buildings). Due to the development of open-source AI technologies, the increase in available Unmanned Aerial Vehicles (UAVs) and the availability of smartphone cameras, it has become possible to automate the building crack inspection process. This study presents the development of an easy-to-use, free and open-source Automated Building Exterior Crack Inspection Software (ABECIS) for construction and facility managers, using state-of-the-art segmentation algorithms to identify concrete cracks and generate a quantitative and qualitative report. ABECIS was tested using images collected from a UAV and smartphone cameras in real-world conditions and a controlled laboratory environment. From the raw output of the algorithm, the median Intersection over Unions for the test experiments is (1) 0.686 for indoor crack detection experiment in a controlled lab environment using a commercial drone, (2) 0.186 for indoor crack detection at a construction site using a smartphone and (3) 0.958 for outdoor crack detection on university campus using a commercial drone. These IoU results can be improved significantly to over 0.8 when a human operator selectively removes the false positives. In general, ABECIS performs best for outdoor drone images, and combining the algorithm predictions with human verification/intervention offers very accurate crack detection results. The software is available publicly and can be downloaded for out-of-the-box use at: https://github.com/SMART-NYUAD/ABECIS