Accesso libero

Automatic Crack Detection Using Weakly Supervised Semantic Segmentation Network and Mixed-Label Training Strategy

INFORMAZIONI SU QUESTO ARTICOLO

Cita

Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[J]. Advances in neural information processing systems, 2012, 25. Search in Google Scholar

Kim B, Cho S. Image-based concrete crack assessment using mask and region-based convolutional neural network Structural Control and Health Monitoring, 26, 8, 2019, e2381. Search in Google Scholar

Koch C, Georgieva K, Kasireddy V, et al. A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure Advanced Engineering Informatics, 29, 2, 2015, 196-210. Search in Google Scholar

Cheng H, Li Y, Li H, et al. Embankment crack detection in UAV images based on efficient channel attention U2Net Structures, 50, 2023, 430-443. Search in Google Scholar

Chen Z, Wang T, Wu X, et al. Class re-activation maps for weakly-supervised semantic segmentation IEEE Transactions on Intelligent Transportation System-sProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, 969-978. Search in Google Scholar

Chang Y T, Wang Q, Hung W C, et al. Weakly-supervised semantic segmentation via sub-category exploration Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, 8991-9000. Search in Google Scholar

Tomasi C, Manduchi R. Bilateral filtering for gray and color images Sixth international conference on computer vision, 1998: 839-846. Search in Google Scholar

Pathak D, Krahenbuhl P, Darrell T. Constrained convolutional neural networks for weakly supervised segmentation Proceedings of the IEEE international conference on computer vision, 2015, 1796-1804. Search in Google Scholar

Dong Z, Wang J, Cui B, et al. Patch-based weakly supervised semantic segmentation network for crack detection Construction and Building Materialsg, 258, 2020, 120291. Search in Google Scholar

Fan Z, Wu Y, Lu J, et al. Automatic pavement crack detection based on structured prediction with the convolutional neural network arXiv preprint arXiv, 2018, 1802.02208. Search in Google Scholar

Fan R, Bocus M J, Zhu Y, et al. Road crack detection using deep convolutional neural network and adaptive thresholding 2019 IEEE Intelligent Vehicles Symposium (IV), 2019, 474-479. Search in Google Scholar

Gong Q, Zhu L, Wang Y, et al. Automatic subway tunnel crack detection system based on line scan camera Structural Control and Health Monitoring, 28, 8, 2021, e2776. Search in Google Scholar

Oliveira H, Correia P L. Automatic road crack segmentation using entropy and image dynamic thresholding 2009 17th European Signal Processing Conference, 2009, 622-626. Search in Google Scholar

Božič J, Tabernik D, Skočaj D. Mixed supervision for surface-defect detection: From weakly to fully supervised learning Computers in Industry, 129, 2021, 103459. Search in Google Scholar

König J, Jenkins M D, Mannion M, et al. Weakly-supervised surface crack segmentation by generating pseudo-labels using localization with a classifier and thresholding IEEE Transactions on Intelligent Transportation Systems, 23, 12, 2022, 24083-24094. Search in Google Scholar

Ahn J, Kwak S. Learning pixel-level semantic a nity with image-level supervision for weakly supervised semantic segmentation Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, 4981-4990. Search in Google Scholar

Jiang W, Liu M, Peng Y, et al. HDCB-Net: A neural network with the hybrid dilated convolution for pixel-level crack detection on concrete bridges IEEE Transactions on Industrial Informatics, 17, 8, 2020, 5485-5494. Search in Google Scholar

Liu Y, Yao J, Lu X, et al. DeepCrack: A deep hierarchical feature learning architecture for crack segmentation Neurocomputing, 338, 2019, 139-153. Search in Google Scholar

Li Q, Zou Q, Zhang D, et al. FoSA: F* seed-growing approach for crack-line detection from pavement images Image and Vision Computing, 29, 12, 2011, 861-872. Search in Google Scholar

Iraniparast M, Ranjbar S, Rahai M, et al. Surface concrete cracks detection and segmentation using transfer learning and multi-resolution image processing Structures, 54, 2023, 386-398. Search in Google Scholar

Liu H, Miao X, Mertz C, et al. Crackformer: Transformer network for fine-grained crack detection Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 3783-3792. Search in Google Scholar

Abdel-Qader I, Abudayyeh O, Kelly M E. Analysis of edge-detection techniques for crack identification in bridges Journal of Computing in Civil Engineering, 17, 4, 2003, 255-263. Search in Google Scholar

Nie M, Wang C. Pavement Crack Detection based on yolo v3 2019 2nd international conference on safety produce informatization (IICSPI), 2019: 327-330. Search in Google Scholar

Nigam R, Singh S K. Crack detection in a beam using wavelet transform and photographic measurements Structures, 25, 2020, 436-447. Search in Google Scholar

Otsu N. A threshold selection method from gray-level histograms IEEE transactions on systems, man, and cybernetics, 9, 1, 1979, 62-66. Search in Google Scholar

Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015, 2015, 234-241. Search in Google Scholar

Woo S, Park J, Lee J Y, et al. Cbam: Convolutional block attention module Proceedings of the European conference on computer vision (ECCV), 2018, 3-19. Search in Google Scholar

Durand T, Mordan T, Thome N, et al. Wildcat: Weakly supervised learning of deep convnets for image classification, pointwise localization and segmentation Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, 642-651. Search in Google Scholar

Wang K C P, Li Q, Gong W. Wavelet-based pavement distress image edge detection with a trous algorithm Transportation Research Record, 2024, 1, 2007, 73-81. Search in Google Scholar

Wang H, Li Y, Dang L M, et al. Pixel-level tunnel crack segmentation using a weakly supervised annotation approach[J]. Computers in Industry, 2021, 133: 103545. Search in Google Scholar

Wang M, Cheng J C P. A unified convolutional neural network integrated with conditional random field for pipe defect segmentation Computer-Aided Civil and Infrastructure Engineering, 35, 2, 2020, 162-177. Search in Google Scholar

Yang F, Zhang L, Yu S, et al. Feature pyramid and hierarchical boosting network for pavement crack detection IEEE Transactions on Intelligent Transportation Systems, 21, 4, 2019, 1525-1535. Search in Google Scholar

Zou Q, Zhang Z, Li Q, et al. Deepcrack: Learning hierarchical convolutional features for crack detection IEEE transactions on image processing, 28, 3, 2018, 1498-1512. Search in Google Scholar

Zhao H, Qin G, Wang X. Improvement of canny algorithm based on pavement edge detection 2010 3rd international congress on image and signal processing, 2, 2010, 964-967. Search in Google Scholar

Zheng S, Jayasumana S, Romera-Paredes B, et al. Conditional random fields as recurrent neural networks Proceedings of the IEEE international conference on computer vision, 2015, 1529-1537. Search in Google Scholar

Zhou B, Khosla A, Lapedriza A, et al. Learning deep features for discriminative localization Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, 2921-2929. Search in Google Scholar

eISSN:
2300-3405
Lingua:
Inglese
Frequenza di pubblicazione:
4 volte all'anno
Argomenti della rivista:
Computer Sciences, Artificial Intelligence, Software Development