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ARTICLE

Slope Collapse Detection Method Based on Deep Learning Technology

Xindai An1, Di Wu1,2,*, Xiangwen Xie1, Kefeng Song1
1 Yellow River Engineering Consulting Co., Ltd., Zhengzhou, 450003, China
2 School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China
* Corresponding Author: Di Wu. Email:
(This article belongs to this Special Issue: Enabled and Human-centric Computational Intelligence Solutions for Visual Understanding and Application)

Computer Modeling in Engineering & Sciences 2023, 134(2), 1091-1103. https://doi.org/10.32604/cmes.2022.020670

Received 06 December 2021; Accepted 15 March 2022; Issue published 31 August 2022

Abstract

So far, slope collapse detection mainly depends on manpower, which has the following drawbacks: (1) low reliability, (2) high risk of human safe, (3) high labor cost. To improve the efficiency and reduce the human investment of slope collapse detection, this paper proposes an intelligent detection method based on deep learning technology for the task. In this method, we first use the deep learning-based image segmentation technology to find the slope area from the captured scene image. Then the foreground motion detection method is used for detecting the motion of the slope area. Finally, we design a lightweight convolutional neural network with an attention mechanism to recognize the detected motion object, thus eliminating the interference motion and increasing the detection accuracy rate. Experimental results on the artificial data and relevant scene data show that the proposed detection method can effectively identify the slope collapse, which has its applicative value and brilliant prospect.

Keywords

Deep learning; slope collapse; image segmentation; image recognition

Cite This Article

An, X., Wu, D., Xie, X., Song, K. (2023). Slope Collapse Detection Method Based on Deep Learning Technology. CMES-Computer Modeling in Engineering & Sciences, 134(2), 1091–1103.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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