2015 Joint Urban Remote Sensing Event (JURSE) 2015
DOI: 10.1109/jurse.2015.7120521
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Manhole cover detection using a geometrical filter on very high resolution aerial and satellite images

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 … Show more

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Cited by 6 publications
(5 citation statements)
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“…To correct this problem, we are considering combining the CNN to a circular filter presented in (Bartoli et al, 2015). Instead of training the network with only the three RGB channels, it will be trained on four entries with the fourth being the result obtained with the circular filter.…”
Section: Resultsmentioning
confidence: 99%
See 2 more Smart Citations
“…To correct this problem, we are considering combining the CNN to a circular filter presented in (Bartoli et al, 2015). Instead of training the network with only the three RGB channels, it will be trained on four entries with the fourth being the result obtained with the circular filter.…”
Section: Resultsmentioning
confidence: 99%
“…Recently a circular geometrical filter based method was proposed in (Bartoli et al, 2015) to detect round-shaped manhole covers in very high resolution aerial images. After the segmentation of the image to retain only the road network, colorimetric indices are used to eliminate the vegetation and shadows.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Bartoli et al [20] reconstructed urban drainage networks by detecting manhole covers using a circular detection filter on orthophotos and satellite images. Pasquet et al [21] identified the presence of underground utilities by locating manhole covers and stormwater drains from high-resolution aerial images using a linear support vector machine (SVM) classifier and a histogram of oriented gradients (HOG) that performed similarly to the circular detection filter used in Bartoli et al [20].…”
Section: Aerial Mappingmentioning
confidence: 99%
“…However in, the above references show that how to detect the manholes previously. The authors in [3] only able to detect the cover of manholes using a geometrical filter on very high resolution aerial and satellite images by using gray color object detection and the success rate is very low about 42%. In [4] two techniques is applied to detect the manholes first one searches the circular pattern using the Bhattacharyya coefficient while another one uses for the machine learning to construct a model of manhole cover the accuracy rate of this algorithm is also not good only 40% covers were detected.…”
Section: Introductionmentioning
confidence: 99%