| Paper ID | SAM-12.5 |
| Paper Title |
Automatic Fine-grained Localization of Utility Pole Landmarks on Distributed Acoustic Sensing Traces Based on Bilinear ResNets |
| Authors |
You Lu, Virginia Tech, United States; Yue Tian, Shaobo Han, Eric Cosatto, Sarper Ozharar, Yangmin Ding, NEC Laboratories America, Inc., United States |
| Session | SAM-12: Tracking and Localization |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Sensor Array and Multichannel Signal Processing: [RAS-DTCL] Target detection, classification, localization |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
