Automatic Fine-Grained Localization of Utility Pole Landmarks on Distributed Acoustic Sensing Traces Based on Bilinear Resnets

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Publication Date: 6/6/2021

Event: 2021 IEEE International Conference on Acoustics, Speech and Signal Processing

Reference: pp. 4675-4679, 2021

Authors: You Lu, NEC Laboratories America, Inc., Virginia Tech; Yue Tian, NEC Laboratories America, Inc.; Shaobo Han, NEC Laboratories America, Inc.; Eric Cosatto, NEC Laboratories America, Inc.; Sarper Ozharar, NEC Laboratories America, Inc.; Yangmin Ding, NEC Laboratories America, Inc.

Abstract: In distributed acoustic sensing (DAS) on aerial fiber-optic cables, utility pole localization is a prerequisite for any subsequent event detection. Currently, localizing the utility poles on DAS traces relies on human experts who manually label the poles’ locations by examining DAS signal patterns generated in response to hammer knocks on the poles. This process is inefficient, error-prone and expensive, thus impractical and non-scalable for industrial applications. In this paper, we propose two machine learning approaches to automate this procedure for large-scale implementation. In particular, we investigate both unsupervised and supervised methods for fine-grained pole localization. Our methods are tested on two real-world datasets from field trials, and demonstrate successful estimation of pole locations at the same level of accuracy as human experts and strong robustness to label noises.

Publication Link: https://ieeexplore.ieee.org/document/9415049