Deep convolutional neural network processing of aerial stereo imagery to monitor vulnerable zones near power lines

Abdul Qayyum; Naufal M. Saad; Nidal Kamel; Aamir Saeed Malik

doi:10.1117/1.JRS.12.014001

14 February 2018 Deep convolutional neural network processing of aerial stereo imagery to monitor vulnerable zones near power lines

Abdul Qayyum, Naufal M. Saad, Nidal Kamel, Aamir Saeed Malik

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 1, 014001 (February 2018). https://doi.org/10.1117/1.JRS.12.014001

Abstract

The monitoring of vegetation near high-voltage transmission power lines and poles is tedious. Blackouts present a huge challenge to power distribution companies and often occur due to tree growth in hilly and rural areas. There are numerous methods of monitoring hazardous overgrowth that are expensive and time-consuming. Accurate estimation of tree and vegetation heights near power poles can prevent the disruption of power transmission in vulnerable zones. This paper presents a cost-effective approach based on a convolutional neural network (CNN) algorithm to compute the height (depth maps) of objects proximal to power poles and transmission lines. The proposed CNN extracts and classifies features by employing convolutional pooling inputs to fully connected data layers that capture prominent features from stereo image patches. Unmanned aerial vehicle or satellite stereo image datasets can thus provide a feasible and cost-effective approach that identifies threat levels based on height and distance estimations of hazardous vegetation and other objects. Results were compared with extant disparity map estimation techniques, such as graph cut, dynamic programming, belief propagation, and area-based methods. The proposed method achieved an accuracy rate of 90%.

Citation Download Citation

Abdul Qayyum, Naufal M. Saad, Nidal Kamel, and Aamir Saeed Malik "Deep convolutional neural network processing of aerial stereo imagery to monitor vulnerable zones near power lines," Journal of Applied Remote Sensing 12(1), 014001 (14 February 2018). https://doi.org/10.1117/1.JRS.12.014001

Received: 29 July 2017; Accepted: 19 January 2018; Published: 14 February 2018

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