Location monitoring approach of underground pipelines using time-sequential images

Abstract

The location monitoring of underground pipelines is of utmost significance as it helps the effective management and maintenance of the pipelines, and facilitates the planning of nearby projects, preventing damage to the pipelines. However, currently there is a serious lack of data on the locations of underground pipelines. This paper proposes an image-based approach for monitoring the locations of underground pipelines by combing deep learning and visual-based reconstruction. The proposed approach can build the monitoring model for underground pipelines and characterize their locations through their centroid curve. Its advantages are: (1) simplicity: it only requires time-sequential images of the inner walls of underground pipelines; (2) clarity: the location model and the location curve of underground pipelines can be provided quickly; (3) robustness: it can cope with some existing problems in underground pipelines, such as light variations and small viewing angles. A lightweight approach for monitoring the locations of underground pipelines is achieved. The proposed approach’s effectiveness has been validated through laboratory simulation experiments, demonstrating accuracy at the millimeter level.

Keywords

Underground pipelines / Location monitoring / Time-sequential images / Visual-based reconstruction / Deep learning

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

The project was supported by the Fundamental Research Funds for the Central Universities (Grant No. 2242023K5006).

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