TY - JOUR
T1 - Deep Learning for Concrete Crack Detection and Measurement
AU - Nyathi, Mthabisi
AU - Bai, Jiping
AU - Wilson, Ian
PY - 2024/2/5
Y1 - 2024/2/5
N2 - Concrete structures inevitably experience cracking, which is a common form of damage. If cracks are left undetected and allowed to worsen, catastrophic failures, with costly implications for human life and the economy, can occur. Traditional image processing techniques for crack detection and measurement have several limitations, which include complex parameter selection and restriction to measuring cracks in pixels, rather than more practical units of millimetres. This paper presents a three-stage approach that utilises deep learning and image processing for crack classification, segmentation and measurement. In the first two stages, custom CNN and U-Net models were employed for crack classification and segmentation. The final stage involved measuring crack width in millimetres by using a novel laser calibration method. The classification and segmentation models achieved 99.22% and 96.54% accuracy, respectively, while the mean absolute error observed for crack width measurement was 0.16 mm. The results demonstrate the adequacy of the developed crack detection and measurement method, and shows the developed deep learning and laser calibration method promotes safer, quicker inspections that are less prone to human error. The method’s ability to measure cracks in millimetres provides a more insightful assessment of structural damage, which is, in comparison to traditional pixel-based measurement methods, a significant improvement for practical field applications.
AB - Concrete structures inevitably experience cracking, which is a common form of damage. If cracks are left undetected and allowed to worsen, catastrophic failures, with costly implications for human life and the economy, can occur. Traditional image processing techniques for crack detection and measurement have several limitations, which include complex parameter selection and restriction to measuring cracks in pixels, rather than more practical units of millimetres. This paper presents a three-stage approach that utilises deep learning and image processing for crack classification, segmentation and measurement. In the first two stages, custom CNN and U-Net models were employed for crack classification and segmentation. The final stage involved measuring crack width in millimetres by using a novel laser calibration method. The classification and segmentation models achieved 99.22% and 96.54% accuracy, respectively, while the mean absolute error observed for crack width measurement was 0.16 mm. The results demonstrate the adequacy of the developed crack detection and measurement method, and shows the developed deep learning and laser calibration method promotes safer, quicker inspections that are less prone to human error. The method’s ability to measure cracks in millimetres provides a more insightful assessment of structural damage, which is, in comparison to traditional pixel-based measurement methods, a significant improvement for practical field applications.
KW - concrete crack detection
KW - crack measurement
KW - deep learning
KW - segmentation
KW - laser calibration
KW - image processing
U2 - 10.3390/metrology4010005
DO - 10.3390/metrology4010005
M3 - Article
SN - 2673-8244
VL - 4
SP - 66
EP - 81
JO - Metrology
JF - Metrology
IS - 1
M1 - 4010005
ER -