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Optimal CNN-based semantic segmentation model of cutting slope images

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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.

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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.

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Authors and Affiliations

  1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou, 510006, China

    Mansheng Lin, Shuai Teng, Gongfa Chen & Jianbing Lv

  2. JSTI Group Guangdong Inspection and Certification Co. Ltd, Nanjing, 210000, China

    Zhongyu Hao

Authors
  1. Mansheng Lin
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  2. Shuai Teng
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  3. Gongfa Chen
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  4. Jianbing Lv
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  5. Zhongyu Hao
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Corresponding author

Correspondence to Gongfa Chen.

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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

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  • DOI: https://doi.org/10.1007/s11709-021-0797-6

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