Abstract
Detecting cracks of structures is a crucial role in the structural health monitoring. Destructive techniques and non-destructive approaches have been widely used to evaluate the structural health. Recently, a deep learning-based approach is developed for noncontact inspections. The goal of this paper is to suggest an efficient backbone of Resnet family in terms of crack detections using DeepLabv3+ architecture for the structural health monitoring. Five kinds of backbones, namely Resnet-18, Restnet-34, Resnet-50, Resnet-101, and Resnet-152 were implemented in this study. Adaptive moment estimation (Adam) optimizer and dice loss function were applied to train the models. In addition, the mean intersection over union (IoU) was employed to investigate the accuracy of proposed models. The study results show that all backbones effectively detected the concrete cracks with over 90% IoU. The Resnet-50 presents the best performance of 93.5% IoU for DeepLabv3+ architecture. The findings highlighted the feasibility of proposed method in terms of structural crack detections.
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References
Alokasi H, Ahmad MB (2022) The Accuracy Performance of Semantic Segmentation Network with Different Backbones. In 7th International Conference on Data Science and Machine Learning Applications, 49–54
Chen LC, Papandreou G, Schroff F, Adam H (2017) Rethinking Atrous Convolution for Semantic Image Segmentation, arXiv preprint arXiv:1706.05587
Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. The European conference on computer vision (ECCV). Springer, Cham, pp 833–851
Demir I, Koperski K, Lindenbaum D, Pang G, Huang J, Basu S, Hughes F, Tuia D, Raskar R (2018) DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images. In 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 172–181
Ho TT, Kim GT, Kim T, Choi S, Park EK (2022) Classification of rotator cuff tears in ultrasound images using deep learning models. Med Biol Eng Comput 60(5):1269–1278
Ho TT et al (2021) A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects. Sci Rep 11(1):34
Liu Y, Yao J, Lu X, Xie R, Li L (2019) DeepCrack: A deep hierarchical feature learning architecture for crack segmentation. Neurocomputing 338:139–153
Nassif AB, Shahin I, Attili I, Azzeh M, Shaalan K (2019) Speech Recognition Using Deep Neural Networks: A Systematic Review. IEEE Access 7:19143–19165
Nguyen TN, Le AT, Nguyen MT (2017) Factors Influencing Strength Development in Soft Soil Clay Mixed Rice Husk Ash Based Geopolymer. Adv Exp Mech 2:153–158
Nguyen TN, Le HQP, Le AT (2023) Activation of Nanoparticle and Alkaline Environment on Fly Ash Geopolymer Mortar. International Conference on Sustainable Civil Engineering and Architecture (ICSCEA 2021). Springer, Singapore, pp 361–370
Nguyen TN, Tran VT, Woo SW, Park SS (2022) Image Segmentation of Concrete Cracks Using SegNet. Intelligence of Things: Technologies and Applications. Springer, Cham, pp 348–355
Park SS, Tran VT, Doan NP, Hwang KB (2022) Evaluation of Damage Level for Ground Settlement Using the Convolutional Neural Network. CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure. Springer, Singapore, pp 1261–1268
Park SS, Tran VT, Lee DE (2021) Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection. Appl Sci 11(23):11229
Song Z (2019) English speech recognition based on deep learning with multiple features. Computing 102(3):663–682. https://doi.org/10.1007/s00607-019-00753-0
Tran VT, To TS, Nguyen TN, Tran TD (2022) Safety Helmet Detection at Construction Sites Using YOLOv5 and YOLOR. Intelligence of Things: Technologies and Applications. Springer, Cham, pp 339–347
Zhang R, Du L, Xiao Q, Liu J (2020) Comparison of Backbones for Semantic Segmentation Network. J Phys Conf Ser 1544(1):012196
Acknowledgements
We acknowledge the support from Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for this study.
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Nguyen, TG., Do, TL., Nguyen, TN., Nguyen, NN. (2024). Semantic Segmentation of Cracks Using DeepLabv3+. In: Reddy, J.N., Wang, C.M., Luong, V.H., Le, A.T. (eds) Proceedings of the Third International Conference on Sustainable Civil Engineering and Architecture. ICSCEA 2023. Lecture Notes in Civil Engineering, vol 442. Springer, Singapore. https://doi.org/10.1007/978-981-99-7434-4_165
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DOI: https://doi.org/10.1007/978-981-99-7434-4_165
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