Abstract
This paper utilizes three popular semantic segmentation networks, specifically DeepLab v3+, fully convolutional network (FCN), and U-Net to qualitively analyze and identify the key components of cutting slope images in complex scenes and achieve rapid image-based slope detection. The elements of cutting slope images are divided into 7 categories. In order to determine the best algorithm for pixel level classification of cutting slope images, the networks are compared from three aspects: a) different neural networks, b) different feature extractors, and c) 2 different optimization algorithms. It is found that DeepLab v3+ with Resnet18 and Sgdm performs best, FCN 32s with Sgdm takes the second, and U-Net with Adam ranks third. This paper also analyzes the segmentation strategies of the three networks in terms of feature map visualization. Results show that the contour generated by DeepLab v3+ (combined with Resnet18 and Sgdm) is closest to the ground truth, while the resulting contour of U-Net (combined with Adam) is closest to the input images.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Chen Z. Soil Slope Stability Analysis—Principle, Methods and Programs. Beijing: China Water & Power Press, 2003 (in Chinese)
Wu C I, Kung H Y, Chen C H, Kuo L C. An intelligent slope disaster prediction and monitoring system based on WSN and ANP. Expert Systems with Applications, 2014, 41(10): 4554–4562
Shu J, Zhang J, Wu J. Research on highway slope disaster identification based on deep convolution neural network. Highway Traffic Technology, 2017, 13(10): 70–74 (in Chinese)
Wu J. Feature learning of highway image and detection of slope failure. Thesis for the Master’s Degree. Beijing: Beijing University of Posts and Telecommunications, 2018 (in Chinese)
Xu J, Gui C, Han Q. Recognition of rust grade and rust ratio of steel structures based on ensembled convolutional neural network. Computer-Aided Civil and Infrastructure Engineering, 2020, 35(10): 1160–1174
Guo H, Zhuang X, Rabczuk T. A deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials & Continua, 2019, 59(2): 433–456
Anitescu C, Atroshchenko E, Alajlan N, Rabczuk T. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 2019, 59(1): 345–359
Samaniego E, Anitescu C, Goswami S, Nguyen-Thanh V M, Guo H, Hamdia K, Zhuang X, Rabczuk T. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 2020, 362: 112790
Zhou H, Chen Y, Tian R. Distance prediction of slope-foot landslide in southwest of China based on GA-BP neural network. In: 2019 the 6th Annual International Conference on Material Engineering and Application. Guangzhou: IOP Publishing, 2020
Xing Y, Wang J, Li X, Liu R, Gao J. Slope stability prediction model based on GA-SVM. In: 2010 International Conference on Educational and Information Technology. Chongqing: IEEE, 2010
Lin H M, Chang S K, Wu J H, Juang C H. Neural network-based model for assessing failure potential of highway slopes in the Alishan, Taiwan Area (China): Pre- and post-earthquake investigation. Engineering Geology, 2009, 104(3–4): 280–289
Xia Y, Chen B, Weng S, Ni Y Q, Xu Y L. Temperature effect on vibration properties of civil structures: A literature review and case studies. Journal of Civil Structural Health Monitoring, 2012, 2(1): 29–46
Yao X. Evolutionary artificial neural networks. International Journal of Neural Systems, 1993, 4(3): 203–222
Lin Y, Nie Z, Ma H. Structural damage detection with automatic feature-extraction through deep learning. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(12): 1025–1046
Zhong K, Teng S, Liu G, Chen G, Cui F. Structural damage features extracted by convolutional neural networks from mode shapes. Applied Sciences (Basel, Switzerland), 2020, 10(12): 4247–4262
Teng S, Liu Z, Chen G, Cheng L. Concrete crack detection based on well-known feature extractor model and the YOLO_v2 network. Applied Sciences (Basel, Switzerland), 2021, 11(2): 813–825
Ghorbanzadeh O, Meena S R, Blaschke T, Aryal J. UAV-based slope failure detection using deep-learning convolutional neural networks. Remote Sensing, 2019, 11(17): 2046–2069
Badrinarayanan V, Kendall A, Cipolla R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481–2495
Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI). Munich: Springer, 2015: 234–241
Chen L C, Zhu Y, Papandreou G, Schroff F, Adam H. Encoderdecoder with atrous separable convolution for semantic image segmentation. In: European Conference on Computer Vision (ECCV). Munich: Springer, 2018: 833–851
Shelhamer E, Long J, Darrell T. Fully Convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640–651
Narazaki Y, Hoskere V, Hoang T A, Fujino Y, Sakurai A, Spencer B F Jr. Vision-based automated bridge component recognition with high-level scene consistency. Computer-Aided Civil and Infrastructure Engineering, 2020, 35(5): 465–482
Liu J, Yang X, Lau S, Wang X, Luo S, Lee V C S, Ding L. Automated pavement crack detection and segmentation based on two-step convolutional neural network. Computer-Aided Civil and Infrastructure Engineering, 2020, 35(11): 1291–1305
Dung C V, Anh L D. Autonomous concrete crack detection using deep fully convolutional neural network. Automation in Construction, 2019, 99: 52–58
Teng S, Chen G, Gong P, Liu G, Cui F. Structural damage detection using convolutional neural networks combining strain energy and dynamic response. Meccanica, 2020, 55(4): 945–959
Rojahn C, Bonneville D R, Quadri N D, Phipps M T, Ranous R A, Russell J E, Staehlin W E, Turner Z. Postearthquake Safety Evaluation of Buildings. Redwood City, CA: Applied Technology Council, 2005
Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation. In: 2015 IEEE International Conference on Computer Vision (ICCV). Las Condes: IEEE, 2015: 1520–1528
Dong C, Loy C C, Tang X. Accelerating the super-resolution convolutional neural network. In: European Conference on Computer Vision (ECCV). Amsterdam: Springer, 2016: 391–407
Nguyen-Thanh V M, Anitescu C, Alajlan N, Rabczuk T, Zhuang X. Parametric deep energy approach for elasticity accounting for strain gradient effects. Computer Methods in Applied Mechanics and Engineering, 2021, 386: 114096
Zhuang X, Guo H, Alajlan N, Zhu H, Rabczuk T. Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning. European Journal of Mechanics. A, Solids, 2021, 87: 104225
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV: IEEE, 2016: 770–778
Chen L C, Papandreou G, Kokkinos I, Murphy K, Yuille A L. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834–848
Cha Y J, Choi W, Büyüköztürk O. Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(5): 361–378
He K, Zhang X, Ren S, Sun J. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: 2015 IEEE International Conference on Computer Vision (ICCV). Las Condes: IEEE, 2015: 1026–1034
Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. 2015, arXiv:1502.03167
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 2014, 15(1): 1929–1958
Csurka G, Larlus D, Perronnin F. What is a good evaluation measure for semantic segmentation? In: Proceedings of the British Machine Vision Conference. Bristol: BMVA, 2013
Randall Wilson D, Martinez T R. The need for small learning rates on large problems. In: International Joint Conference on Neural Networks. Washington, D.C.: IEEE, 2001: 115–119
Krogh A, Hertz J A. A Simple Weight Decay Can Improve Generalization. In: Proceedings of the 4th International Conference on Neural Information Processing Systems (NIPS). Denver: MIT Press, 1991
David Eigen R F. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: IEEE International Conference on Computer Vision (ICCV). Las Condes: IEEE, 2015
Zhang Y, Yang Y. Cross-validation for selecting a model selection procedure. Journal of Econometrics, 2015, 187(1): 95–112
Acknowledgements
The authors would like to express their sincere gratitude to Yang HE, Wei DENG, Ronghao ZHANG, from the Guangdong University of Technology, for labeling the image data.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lin, M., Teng, S., Chen, G. et al. Optimal CNN-based semantic segmentation model of cutting slope images. Front. Struct. Civ. Eng. 16, 414–433 (2022). https://doi.org/10.1007/s11709-021-0797-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11709-021-0797-6