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Journal Home Online First Current Issue Archive For Authors Journal Information 中文版

2022, Volume 16, Issue 1

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Frontiers of Structural and Civil Engineering doi: 10.1007/s11709-021-0797-6

Optimal CNN-based semantic segmentation model of cutting slope images

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Available online:2022-06-27

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.

Keywords

slope damage ; image recognition ; semantic segmentation ; feature map ; visualizations

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Website Copyright © 2015 China Engineering Science Press Co., Ltd.
京公网安备 11010502051620号 京ICP备11030251号-2