@ARTICLE{Grabowski_Dariusz_Economical_2018,
 author={Grabowski, Dariusz and Szczodrak, Maciej and Czyżewski, Andrzej},
 volume={vol. 25},
 number={No 3},
 journal={Metrology and Measurement Systems},
 pages={533-549},
 howpublished={online},
 year={2018},
 publisher={Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation},
 abstract={Two low-cost methods of estimating the road surface condition are presented in the paper, the first one based on the use of accelerometers and the other on the analysis of images acquired from cameras installed in a vehicle. In the first method, miniature positioning and accelerometer sensors are used for evaluation of the road surface roughness. The device designed for installation in vehicles is composed of a GPS receiver and a multi-axis accelerometer. The measurement data were collected from recorded ride sessions taken place on diversified road surface roughness conditions and at varied vehicle speeds on each of examined road sections. The data were gathered for various vehicle body types and afterwards successful attempts were made in constructing the road surface classification employing the created algorithm. In turn, in the video method, a set of algorithms processing images from a depth camera and RGB cameras were created. A representative sample of the material to be analysed was obtained and a neural network model for classification of road defects was trained. The research has shown high effectiveness of applying the digital image processing to rejection of images of undamaged surface, exceeding 80%. Average effectiveness of identification of road defects amounted to 70%. The paper presents the methods of collecting and processing the data related to surface damage as well as the results of analyses and conclusions.},
 type={Artykuły / Articles},
 title={Economical methods for measuring road surface roughness},
 URL={http://ochroma.man.poznan.pl/Content/108106/PDF/art09.pdf},
 doi={10.24425/123905},
 keywords={image processing, neural networks, pothole detection, Kinect, Raspberry Pi},
}