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Economical methods for measuring road surface roughness

Grabowski D., Szczodrak M., Czyżewski A.

Metrology and Measurement Systems

2018

Vol. 25, nr 3

533--549

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.

Badanie stanu nawierzchni drogowej z wykorzystaniem uczenia maszynowego

Grabowski D., Czyżewski A.

Magazyn Autostrady

2018

Nr 8-9

79--84

W artykule opisano budowę systemu informowania o stanie nawierzchni drogowej z wykorzystaniem metod cyfrowego przetwarzania obrazów oraz uczenia maszynowego. Efektem wykonanych prac badawczych jest eksperymentalna platforma, pozwalająca na rejestrację uszkodzeń na drogach, system do analizy, przetwarzania i klasyfikacji danych oraz webowa aplikacja użytkownika do przeglądu stanu nawierzchni w wybranej lokalizacji.

Damage to the road surface is caused by many factors: from atmospheric conditions to high traffic to erosion. Poor road conditions cause damage to vehicles, high fuel consumption and accidents. Investigations of this condition, due to their high costs, are often performed manually. The steps of designing and implementing a system for the automatic identification of road pavement and creating a web application for informing the user about the road condition are presented in the paper. A set of algorithms for processing RGB and depth images was created. A neural network model has been trained and used to classify road defects. The obtained research results show 83% efficiency of using digital image processing in discarding images without any damages. In the case of pavement defects classification, the achieved average efficiency approximated 70%.