Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 561011
Title Towards autonomous detection and tracking of electric towers for aerial power line inspection
Author(s) Martinez, Carol; Sampedro, Carlos; Chauhan, Aneesh; Campoy, Pascual
Source In: 2014 International Conference on Unmanned Aircraft Systems, ICUAS 2014 - Conference Proceedings. - IEEE computer society (2014 International Conference on Unmanned Aircraft Systems, ICUAS 2014 - Conference Proceedings ) - ISBN 9781479923762 - p. 284 - 295.
Event 2014 International Conference on Unmanned Aircraft Systems, ICUAS 2014, Orlando, FL, 2014-05-27/2014-05-30
Publication type Contribution in proceedings
Publication year 2014

This paper presents an approach towards autonomous aerial power line inspection. In particular, the presented work focuses on real-time autonomous detection, localization and tracking of electric towers. A strategy which combines classic computer vision and machine learning techniques, is proposed. A generalized detection and localization approach is presented, where a two-class multilayer perceptron (MLP) neural network was trained for Tower-Background classification. This MLP is applied over sliding windows for each camera frame until a tower is detected. The detection of a tower triggers the tracker. A hierarchical tracking methodology, especially designed for tracking towers in real-time, is presented. This methodology is based on the Hierarchical Multi-Parametric and Multi-Resolution Inverse Compositional Algorithm [1], and is proposed to be used for tracking and maintaining the tower in the field of view (FOV). The proposed strategy, which is the combination of the tower detector and the tracker, is evaluated on videos from several real manned helicopter inspections. Overall, the results show that the proposed strategy performs very well at detecting and tracking various types of electric towers in diverse environmental settings.

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