Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Bayesian-optimized unsupervised semantic segmentation model for structural crack detection
https://orcid.org/0000-0002-5415-6188
To reduce the reliance of existing mainstream vision-based crack detection algorithms on annotated datasets, this paper presents an unsupervised semantic segmentation-based approach. The proposed method first employs the Felzenszwalb-Huttenlocher algorithm for pre-segmenting the image, generating superpixels. Subsequently, a with an autoencoder model is designed to progressively approximate the superpixel segmentation results, and the optimal model is obtained by Bayesian optimization. Through comparative experiments with existing algorithms, it has been demonstrated that the proposed method performance comparable to supervised algorithms, even without the need for labeled data. As a result, the deployment complexity of the algorithm is significantly reduced, while expanding its applicability.
Bayesian-optimized unsupervised semantic segmentation model for structural crack detection
https://orcid.org/0000-0002-5415-6188
To reduce the reliance of existing mainstream vision-based crack detection algorithms on annotated datasets, this paper presents an unsupervised semantic segmentation-based approach. The proposed method first employs the Felzenszwalb-Huttenlocher algorithm for pre-segmenting the image, generating superpixels. Subsequently, a with an autoencoder model is designed to progressively approximate the superpixel segmentation results, and the optimal model is obtained by Bayesian optimization. Through comparative experiments with existing algorithms, it has been demonstrated that the proposed method performance comparable to supervised algorithms, even without the need for labeled data. As a result, the deployment complexity of the algorithm is significantly reduced, while expanding its applicability.
Bayesian-optimized unsupervised semantic segmentation model for structural crack detection
https://orcid.org/0000-0002-5415-6188
To reduce the reliance of existing mainstream vision-based crack detection algorithms on annotated datasets, this paper presents an unsupervised semantic segmentation-based approach. The proposed method first employs the Felzenszwalb-Huttenlocher algorithm for pre-segmenting the image, generating superpixels. Subsequently, a with an autoencoder model is designed to progressively approximate the superpixel segmentation results, and the optimal model is obtained by Bayesian optimization. Through comparative experiments with existing algorithms, it has been demonstrated that the proposed method performance comparable to supervised algorithms, even without the need for labeled data. As a result, the deployment complexity of the algorithm is significantly reduced, while expanding its applicability.
Bayesian-optimized unsupervised semantic segmentation model for structural crack detection
Sun, Qixin (Autor:in) / Zheng, Ruiping (Autor:in) / Xu, Boqiang (Autor:in)
Advances in Structural Engineering ; 27 ; 432-449
01.02.2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch