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Crack Detection Based on Generative Adversarial Networks and Deep Learning
https://orcid.org/https://orcid.org/0000-0001-7232-9583
https://orcid.org/https://orcid.org/0000-0003-1703-3362
This paper proposes a novel crack detection method using the three-stages detection model. Deep learning technology has been a focus of attention in the field of crack detection; however, it needs big data to train the corresponding network model. More training samples and the combination of multiple deep learning algorithms help to improve the detection performance. Therefore, this paper employed a generative adversarial network (GAN) model to generate abundant virtual crack images with similar features to real images, these virtual images are used to train the CNN classifier and DeepLab_v3+ respectively, and then the real images are used to evaluate the performance of the three-stages detection method. The results show that the proposed three-stages detection method has excellent detection effect on the crack detection is better than that of the control experiment (the NI_MIoU, NI_Accuracy, NI_F-score and NI_MCC are increased by 22.1%–55.6%, 5.2%–9.8%, 37.4%–40.0% and 6.2%–11.1% respectively)). These results demonstrate that the three-stages detection model has made a beneficial contribution to the crack detection.
Crack Detection Based on Generative Adversarial Networks and Deep Learning
https://orcid.org/https://orcid.org/0000-0001-7232-9583
https://orcid.org/https://orcid.org/0000-0003-1703-3362
This paper proposes a novel crack detection method using the three-stages detection model. Deep learning technology has been a focus of attention in the field of crack detection; however, it needs big data to train the corresponding network model. More training samples and the combination of multiple deep learning algorithms help to improve the detection performance. Therefore, this paper employed a generative adversarial network (GAN) model to generate abundant virtual crack images with similar features to real images, these virtual images are used to train the CNN classifier and DeepLab_v3+ respectively, and then the real images are used to evaluate the performance of the three-stages detection method. The results show that the proposed three-stages detection method has excellent detection effect on the crack detection is better than that of the control experiment (the NI_MIoU, NI_Accuracy, NI_F-score and NI_MCC are increased by 22.1%–55.6%, 5.2%–9.8%, 37.4%–40.0% and 6.2%–11.1% respectively)). These results demonstrate that the three-stages detection model has made a beneficial contribution to the crack detection.
Crack Detection Based on Generative Adversarial Networks and Deep Learning
https://orcid.org/https://orcid.org/0000-0001-7232-9583
https://orcid.org/https://orcid.org/0000-0003-1703-3362
This paper proposes a novel crack detection method using the three-stages detection model. Deep learning technology has been a focus of attention in the field of crack detection; however, it needs big data to train the corresponding network model. More training samples and the combination of multiple deep learning algorithms help to improve the detection performance. Therefore, this paper employed a generative adversarial network (GAN) model to generate abundant virtual crack images with similar features to real images, these virtual images are used to train the CNN classifier and DeepLab_v3+ respectively, and then the real images are used to evaluate the performance of the three-stages detection method. The results show that the proposed three-stages detection method has excellent detection effect on the crack detection is better than that of the control experiment (the NI_MIoU, NI_Accuracy, NI_F-score and NI_MCC are increased by 22.1%–55.6%, 5.2%–9.8%, 37.4%–40.0% and 6.2%–11.1% respectively)). These results demonstrate that the three-stages detection model has made a beneficial contribution to the crack detection.
Crack Detection Based on Generative Adversarial Networks and Deep Learning
KSCE J Civ Eng
Chen, Gongfa (author) / Teng, Shuai (author) / Lin, Mansheng (author) / Yang, Xiaomei (author) / Sun, Xiaoli (author)
KSCE Journal of Civil Engineering ; 26 ; 1803-1816
2022-04-01
14 pages
Article (Journal)
Electronic Resource
English
| Elsevier | 2023