Developing a Free and Open-Source Semi-Automated Building Exterior Crack Inspection Software for Construction and Facility Managers
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...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10188406/ |
| _version_ | 1797768436016742400 |
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| author | Pi Ko Samuel A. Prieto Borja Garcia de Soto |
| author_facet | Pi Ko Samuel A. Prieto Borja Garcia de Soto |
| author_sort | Pi Ko |
| collection | DOAJ |
| description | 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 (IoU) for the test experiments are (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. |
| first_indexed | 2024-03-12T20:53:28Z |
| format | Article |
| id | doaj.art-c7cb1f9ce09745108459fc573a35acea |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T20:53:28Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c7cb1f9ce09745108459fc573a35acea2023-07-31T23:01:12ZengIEEEIEEE Access2169-35362023-01-0111770997711610.1109/ACCESS.2023.329679310188406Developing a Free and Open-Source Semi-Automated Building Exterior Crack Inspection Software for Construction and Facility ManagersPi Ko0https://orcid.org/0000-0001-8798-8055Samuel A. Prieto1https://orcid.org/0000-0001-8341-2630Borja Garcia de Soto2https://orcid.org/0000-0002-9613-8105S.M.A.R.T. Construction Research Group, Division of Engineering, New York University Abu Dhabi (NYUAD), Experimental Research Building, Abu Dhabi, United Arab EmiratesS.M.A.R.T. Construction Research Group, Division of Engineering, New York University Abu Dhabi (NYUAD), Experimental Research Building, Abu Dhabi, United Arab EmiratesS.M.A.R.T. Construction Research Group, Division of Engineering, New York University Abu Dhabi (NYUAD), Experimental Research Building, Abu Dhabi, United Arab EmiratesInspection 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 (IoU) for the test experiments are (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.https://ieeexplore.ieee.org/document/10188406/Building inspectionconstruction automationdeep learningDetectron2image processingsegmentation |
| spellingShingle | Pi Ko Samuel A. Prieto Borja Garcia de Soto Developing a Free and Open-Source Semi-Automated Building Exterior Crack Inspection Software for Construction and Facility Managers IEEE Access Building inspection construction automation deep learning Detectron2 image processing segmentation |
| title | Developing a Free and Open-Source Semi-Automated Building Exterior Crack Inspection Software for Construction and Facility Managers |
| title_full | Developing a Free and Open-Source Semi-Automated Building Exterior Crack Inspection Software for Construction and Facility Managers |
| title_fullStr | Developing a Free and Open-Source Semi-Automated Building Exterior Crack Inspection Software for Construction and Facility Managers |
| title_full_unstemmed | Developing a Free and Open-Source Semi-Automated Building Exterior Crack Inspection Software for Construction and Facility Managers |
| title_short | Developing a Free and Open-Source Semi-Automated Building Exterior Crack Inspection Software for Construction and Facility Managers |
| title_sort | developing a free and open source semi automated building exterior crack inspection software for construction and facility managers |
| topic | Building inspection construction automation deep learning Detectron2 image processing segmentation |
| url | https://ieeexplore.ieee.org/document/10188406/ |
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