Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images.

Weixing Zhang,Jason Parent,Xiaojiang Li,Chuanrong Zhang,Chandi Witharana,Weidong Li

doi:10.3390/s18082484

Weixing Zhang, Jason Parent + Show 4 more

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https://doi.org/10.3390/s18082484

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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.

Highlights

Maintaining the electric grid is a challenging task and accurate maps of utility infrastructure are important for planning and operations, managing risk, and rapidly assessing damages after a storm [1]
For testing the performance of using deep learning to estimate the locations of utility poles with crossarms (UPCs) in Google Street View (GSV)
For testing the performance of using deep learning to estimate the locations of UPCs in images, we conducted experiments on a customized server, which is equipped with an Intel i5 CPU, GSV images, we conducted experiments on a customized server, which is equipped with an Intel

Summary

Introduction

Maintaining the electric grid is a challenging task and accurate maps of utility infrastructure are important for planning and operations, managing risk, and rapidly assessing damages after a storm [1]. After hurricane Maria struck Puerto Rico in September of 2017, the lack of accurate maps for buildings, bridges, and electric facilities was considered as a main factor slowing recovery efforts [3]. The high degree of labor requirement makes mapping utility poles over large areas a daunting task. Remote sensing (RS) provides promising solutions for automated detection and mapping of electric facilities. Utility mapping has been explored using optical sensors, on both satellite and aerial platforms [5,6,7,8,9,10,11,12]; synthetic aperture radars (SAR) [13,14], and light detection and ranging (LiDAR) [15,16,17,18,19]. Wang et al [19]

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