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

Abstract

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.

Manhole Cover Localization in Aerial Images With a Deep Learning Approach

“…The customized network detects almost 50% of the manhole covers in the aerial images. These results are better than those obtained by (Pasquet et al, 2016), using a machine-learning SVM approach jointly with a low level approach. In their case, for a precision of 66% the recall is only of 45% with a simpler database and numerous preprocessing steps, whereas for the same precision value we obtain a recall of 54% without any additional preprocessing.…”

“…Finally, a circular filter is used to locate the manhole covers. In (Pasquet et al, 2016) a framework is proposed to automatically detect manhole covers in high resolution aerial images by combining the method based on the geometrical filter with a machine-learning SVM based approach. Results are encouraging, combination of the circular filter with deep-learning CNN instead of SVM can be envisaged to obtained better results than only use RBG channels.…”

“…The manhole covers and inlet grates which are surface elements and thus indicators of the location of these networks, can be observed on aerial images. Hence, we can rely on the automatic detection of these objects to provide an estimation of the underground utility networks' position (Pasquet et al, 2016).…”

“…This has the advantage of providing a feedback about the current road conditions and thus avoiding accidents whether it is for intelligent transportation systems, moving vehicles or pedestrians (Yu et al, 2015). * Corresponding author carole.delenne@umontpellier.fr But these approaches are extremely expensive, need considerable post-processing and are restricted to objects located on roads.In previous works, we have attempted to detect manhole covers using aerial images and have thus demonstrated the potential of these low cost and efficient methods (Pasquet et al, 2016, Bartoli et al, 2015.…”

Optimizing color information processing inside an SVM network

“…More specifically, we focus on detecting man-hole covers [13] and tombs [14] for geo-localization purposes. In particular, tomb detection appears to be a very challenging problem as tombs vary substantially in appearance, color and size in aerial images.…”