Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images
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sensors-18-02484.pdf
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Checksum (MD5)
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Author(s)
Zhang, Weixing
Witharana, Chandi
Li, Weidong
Zhang, Chuanrong
Li, Xiaojiang
Parent, Jason
Date Issued
July 2018
Journal
Sensors
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Zhang, Weixing, Chandi Witharana, Weidong Li, Chuanrong Zhang, Xiaojiang Li and Jason Parent. "Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images." Sensors 2018, 2484.
Version
Final published version
Abstract
Traditional methods of detecting and mapping utility poles are inefficient and costly because of the demand for visual interpretation with quality data sources or intense field inspection. The advent of deep learning for object detection provides an opportunity for detecting utility poles from side-view optical images. In this study, we proposed using a deep learning-based method for automatically mapping roadside utility poles with crossarms (UPCs) from Google Street View (GSV) images. The method combines the state-of-the-art DL object detection algorithm (i.e., the RetinaNet object detection algorithm) and a modified brute-force-based line-of-bearing (LOB, a LOB stands for the ray towards the location of the target [UPC at here] from the original location of the sensor [GSV mobile platform]) measurement method to estimate the locations of detected roadside UPCs from GSV. Experimental results indicate that: (1) both the average precision (AP) and the overall accuracy (OA) are around 0.78 when the intersection-over-union (IoU) threshold is greater than 0.3, based on the testing of 500 GSV images with a total number of 937 objects; and (2) around 2.6%, 47%, and 79% of estimated locations of utility poles are within 1 m, 5 m, and 10 m buffer zones, respectively, around the referenced locations of utility poles. In general, this study indicates that even in a complex background, most utility poles can be detected with the use of DL, and the LOB measurement method can estimate the locations of most UPCs.
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/s18082484
