Detection of manhole covers in high-resolution aerial images of urban areas by combining two methods

Abstract : The detection of small objects from aerial images is a difficult signal processing task. To localise small objects in an image, low-complexity geometry-based approaches can be used, but their efficiency is often low. Another option is to use appearance-based approaches that give better results but require a costly learning step. In this paper, we treat the specific case of manhole covers. Currently many manholes are not listed or are badly positioned on maps. We implement two conventional previously published methods to detect manhole covers in images. The first one searches for circular patterns in the image while the second uses machine learning to build a model of manhole covers. The results show non optimal performances for each method. The two approaches are combined to overcome this limit, thus increasing the overall performance by about forty percent.
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Communication dans un congrès
JURSE: Joint Urban Remote Sensing Event, Mar 2015, Lausanne, Switzerland. Joint Urban Remote Sensing Event, JURSE'2015, 2015
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01234242
Contributeur : Marc Chaumont <>
Soumis le : jeudi 3 décembre 2015 - 17:37:09
Dernière modification le : lundi 3 septembre 2018 - 14:38:02
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  • HAL Id : lirmm-01234242, version 1

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Jérôme Pasquet, Thibault Desert, Olivier Bartoli, Marc Chaumont, Carole Delenne, et al.. Detection of manhole covers in high-resolution aerial images of urban areas by combining two methods. JURSE: Joint Urban Remote Sensing Event, Mar 2015, Lausanne, Switzerland. Joint Urban Remote Sensing Event, JURSE'2015, 2015. 〈lirmm-01234242〉

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