Fusion of CNN and sparse representation for threat estimation near power lines and poles infrastructure using aerial stereo imagery

Abdul Qayyum, Imran Razzak, Aamir Saeed Malik and Sajid Anwar

Technological Forecasting and Social Change, 2021, vol. 168, issue C

Abstract: Fires or electrical hazards and accidents can occur if vegetation is not controlled or cleared around overhead power lines, resulting in serious risks to people and property and significant costs to the community. There are numerous blackouts due to interfering the trees with the power transmission lines in hilly and urban areas. Power distribution companies are facing a challenge to monitor the vegetation to avoid blackouts and flash-over threats. Recently, several methods have been developed for vegetation monitoring; however, existing methods are either not accurate or could not provide better disparity map in the textureless region. Moreover, are not able to handle depth discontinuity in stereo thus are not able to find a feasible solution in the smooth areas to compute the disparity map. This study presents a cost-effective framework based on UAV and satellite Stereo images to monitor the trees and vegetation, which provide better disparity. We present a novel approach based on the fusion of the convolutional neural network (CNN) and sparse representation that handled textureless region, depth discontinuity and smooth region to produce better disparity map that further used for threat estimation using height and distance of vegetation/trees near power lines and poles. Extensive experimental evaluation on real time powerline monitoring showed considerable imporvemnt in vegetation threat estimation with accuracy of 90.3% in comparison to graph-cut, dynamic programming, belief propagation, and area-based methods.

Keywords: Critical infrastructure; CNN; Sparse representation; Threat estimation; Power lines; Aerial stereo imagery (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:168:y:2021:i:c:s0040162521001943

DOI: 10.1016/j.techfore.2021.120762

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