A real-time framework for detection of long linear infrastructural objects in aerial imagery

Abstract

In any nation, many long linear infrastructures exist, which need to be periodically inspected for faults and subsequent maintenance. These include power grid, pipelines, railway corridor etc. Most of these infrastructures support critical utilities, and hence operated 24 x 7 throughout the year. The length of these infrastructures can be in tens of kilometers. Hence maintenance inspections via acquisition of aerial imagery is gaining popularity. Since such video can have thousands of frames, it is imperative that its analysis be automated. Such infrastructure detection is against a background that is quite heterogeneous and complex. In this paper, we propose an algorithmic framework that can be used for automatic, real-time detection of different linear infrastructural objects in outdoor aerial images. The five-stage algorithm focuses on minimization of false positives. The algorithm was tested against video data captured for two different power grids in outskirts of Bangalore. The results show seldom false positives, and false negatives in certain frames occur sparsely enough that we are able to do a continuous video tracking. We believe that this framework will be useful in real deployments as well. © Springer International Publishing Switzerland 2015.