A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Pore Structure Identification Method for Pervious Concrete Based on Improved UNet and Fusion Algorithm
https://orcid.org/https://orcid.org/0000-0003-4677-2586
https://orcid.org/https://orcid.org/0009-0005-5638-8723
https://orcid.org/https://orcid.org/0000-0002-2154-6673
https://orcid.org/https://orcid.org/0009-0005-3081-8086
https://orcid.org/https://orcid.org/0009-0003-0467-6075
https://orcid.org/https://orcid.org/0000-0003-0403-3144
This paper aims to establish an automatic and accurate pore identification method for pervious concrete. The residual module and mixed loss functions were introduced to the original UNet network to obtain the improved UNet. CT scanning was conducted on the six groups of pervious concrete samples with different aggregate sizes to obtain the initial dataset. The initial dataset was marked and enhanced, and then the pore recognition model was trained. The influence of image brightness and contrast on pore identification was analyzed. The fusion algorithm was used to improve the robustness of the model. The results show that during model training, R-UNet began to converge 20 epochs earlier than UNet and the loss value was smaller. Moreover, the maximum increase of mIoU and mDice was 10.3% and 11.7% respectively, and the maximum decrease of mHD was 14.1%. The fusion algorithm could improve the segmentation accuracy of pores in brightness anomaly images. Compared with threshold segmentation method, the method proposed in this paper could improve the accuracy of pore edge segmentation and the “fine pores” identification, and reduced the pore identification defects. The value of mHD was decreased by 48.7%–72.4%, and the efficiency of pore identification was greatly improved.
Pore Structure Identification Method for Pervious Concrete Based on Improved UNet and Fusion Algorithm
https://orcid.org/https://orcid.org/0000-0003-4677-2586
https://orcid.org/https://orcid.org/0009-0005-5638-8723
https://orcid.org/https://orcid.org/0000-0002-2154-6673
https://orcid.org/https://orcid.org/0009-0005-3081-8086
https://orcid.org/https://orcid.org/0009-0003-0467-6075
https://orcid.org/https://orcid.org/0000-0003-0403-3144
This paper aims to establish an automatic and accurate pore identification method for pervious concrete. The residual module and mixed loss functions were introduced to the original UNet network to obtain the improved UNet. CT scanning was conducted on the six groups of pervious concrete samples with different aggregate sizes to obtain the initial dataset. The initial dataset was marked and enhanced, and then the pore recognition model was trained. The influence of image brightness and contrast on pore identification was analyzed. The fusion algorithm was used to improve the robustness of the model. The results show that during model training, R-UNet began to converge 20 epochs earlier than UNet and the loss value was smaller. Moreover, the maximum increase of mIoU and mDice was 10.3% and 11.7% respectively, and the maximum decrease of mHD was 14.1%. The fusion algorithm could improve the segmentation accuracy of pores in brightness anomaly images. Compared with threshold segmentation method, the method proposed in this paper could improve the accuracy of pore edge segmentation and the “fine pores” identification, and reduced the pore identification defects. The value of mHD was decreased by 48.7%–72.4%, and the efficiency of pore identification was greatly improved.
Pore Structure Identification Method for Pervious Concrete Based on Improved UNet and Fusion Algorithm
https://orcid.org/https://orcid.org/0000-0003-4677-2586
https://orcid.org/https://orcid.org/0009-0005-5638-8723
https://orcid.org/https://orcid.org/0000-0002-2154-6673
https://orcid.org/https://orcid.org/0009-0005-3081-8086
https://orcid.org/https://orcid.org/0009-0003-0467-6075
https://orcid.org/https://orcid.org/0000-0003-0403-3144
This paper aims to establish an automatic and accurate pore identification method for pervious concrete. The residual module and mixed loss functions were introduced to the original UNet network to obtain the improved UNet. CT scanning was conducted on the six groups of pervious concrete samples with different aggregate sizes to obtain the initial dataset. The initial dataset was marked and enhanced, and then the pore recognition model was trained. The influence of image brightness and contrast on pore identification was analyzed. The fusion algorithm was used to improve the robustness of the model. The results show that during model training, R-UNet began to converge 20 epochs earlier than UNet and the loss value was smaller. Moreover, the maximum increase of mIoU and mDice was 10.3% and 11.7% respectively, and the maximum decrease of mHD was 14.1%. The fusion algorithm could improve the segmentation accuracy of pores in brightness anomaly images. Compared with threshold segmentation method, the method proposed in this paper could improve the accuracy of pore edge segmentation and the “fine pores” identification, and reduced the pore identification defects. The value of mHD was decreased by 48.7%–72.4%, and the efficiency of pore identification was greatly improved.
Pore Structure Identification Method for Pervious Concrete Based on Improved UNet and Fusion Algorithm
KSCE J Civ Eng
Yu, Fan (author) / Li, Kailang (author) / Zhang, Hua (author) / Zhang, Rui (author) / Gao, Zhang (author) / Huang, Yubin (author)
KSCE Journal of Civil Engineering ; 27 ; 4834-4848
2023-11-01
15 pages
Article (Journal)
Electronic Resource
English
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