An intelligent system to detect, avoid and maintain potholes: A graph theoretic approach

Venkatesh, Shivanand; Abhiram, E.; Rajarajeswari, S.; Kumar, K. M. Sunil; Balakuntala, Shreyas; Jagadish, Nitin

doi:10.1109/icmu.2014.6799066

Cited by 11 publications

(1 citation statement)

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“…One of them is to use smartphones with accelerometers [1] and to detect the current location with a global positioning system [2]. Another approach is to use pressure sensors that can be built in shock absorbers to detect and quantify the intensity of a pothole [3]. Potholes are detected by using two ultrasonic sensors [4], laser line striper sensors [5], or using hyperspectral imagery [6].…”

Section: Introductionmentioning

confidence: 99%

Detection of the Road Pothole Contour in Raster Images

Jakstys¹,

Marcinkevičius²,

Treigys³

et al. 2016

ITC

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The article analyses the issues related to detection and contour approximation of road surface defects, in particular, road potholes captured by a smart device camera. The assessment of measurements, obtained after image analysis and real objects, will be disclosed with a view to depict the measurement bias. The difference from all the other methods, mentioned in the literature, is that only 2D images captured by one camera were used in this investigation with a view to identify the contour of road potholes and get its parameterised representation. In practice, according to other authors, the most common approach to obtain a parameterised description of a pothole incorporates not only cameras, but also other various sensors such as accelerometer, global positioning systems, laser, hyperspectral imagery, infrared or ultrasonic sensors. In this article, we present a method that allows recognising a pothole object in terms of its colour, shape, and structure. The method discussed was applied to real world images to detect and outline the road pothole contour. Finally, the evaluation of approximation accuracy by empirical research techniques has been accomplished.

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Section: Introductionmentioning

confidence: 99%