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A platform for research: civil engineering, architecture and urbanism
Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning
Abstract This paper proposes an automatic crack detection, localization, and quantification method using the integration of a faster region proposal convolutional neural network (Faster R-CNN) algorithm to detect crack regions. The regions were located using various bounding boxes and a modified tubularity flow field (TuFF) algorithm to segment the crack pixels from the detected crack regions. A modified distance transform method (DTM) was used to measure crack thickness and length in terms of pixel measurement. To validate the proposed method, 100 images were taken in different places with complex backgrounds containing different angles and distances between the camera and the objects. The results obtained from the Faster-R-CNN-based crack damage detection had a 95% average precision. The pixel-level segmentation performance of the modified TuFF algorithm exhibited an authentic outcome, with 83% intersection over union. Finally, the modified DTM algorithm provided 93% accuracy with respect to crack length and thickness with a 2.6 pixel root mean square error.
Highlights The first proposal of an automatic hybrid crack segmentation and quantification under complex backgrounds and different lightening conditions. The hybrid method successfully removes the limitation of independent application of deep learning and image processing method. The proposed method achieved the AP of the Faster R-CNN is 95%, and the IoU of the modified TuFF with CLAHE is 83%. The original DTM method is improved to measure the thickness and length of the segmented cracks with an RMS error of 2.6 pixels, providing crack length accuracy of 93%.
Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning
Abstract This paper proposes an automatic crack detection, localization, and quantification method using the integration of a faster region proposal convolutional neural network (Faster R-CNN) algorithm to detect crack regions. The regions were located using various bounding boxes and a modified tubularity flow field (TuFF) algorithm to segment the crack pixels from the detected crack regions. A modified distance transform method (DTM) was used to measure crack thickness and length in terms of pixel measurement. To validate the proposed method, 100 images were taken in different places with complex backgrounds containing different angles and distances between the camera and the objects. The results obtained from the Faster-R-CNN-based crack damage detection had a 95% average precision. The pixel-level segmentation performance of the modified TuFF algorithm exhibited an authentic outcome, with 83% intersection over union. Finally, the modified DTM algorithm provided 93% accuracy with respect to crack length and thickness with a 2.6 pixel root mean square error.
Highlights The first proposal of an automatic hybrid crack segmentation and quantification under complex backgrounds and different lightening conditions. The hybrid method successfully removes the limitation of independent application of deep learning and image processing method. The proposed method achieved the AP of the Faster R-CNN is 95%, and the IoU of the modified TuFF with CLAHE is 83%. The original DTM method is improved to measure the thickness and length of the segmented cracks with an RMS error of 2.6 pixels, providing crack length accuracy of 93%.
Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning
Kang, Dongho (author) / Benipal, Sukhpreet S. (author) / Gopal, Dharshan L. (author) / Cha, Young-Jin (author)
2020-05-29
Article (Journal)
Electronic Resource
English
Automatic pixel‐level crack detection and evaluation of concrete structures using deep learning
| Wiley | 2022
Concrete Surface Crack Segmentation Based on Deep Learning
| Springer Verlag | 2021