System for monitoring road slippery based on CCTV cameras and convolutional neural networks

Dariusz Grabowski; Andrzej Czyżewski

doi:10.1007/s10844-020-00618-5

System for monitoring road slippery based on CCTV cameras and convolutional neural networks

Abstrakt

The slipperiness of the surface is essential for road safety. The growing number of CCTV cameras opens the possibility of using them to automatically detect the slippery surface and inform road users about it. This paper presents a system of developed intelligent road signs, including a detector based on convolutional neural networks (CNNs) and the transferlearning method employed to the processing of images acquired with video cameras. Based on photos taken in different light conditions by CCTV cameras located at the roadsides in Poland, four network topologies have been trained and tested: Resnet50 v2, Resnet152 v2, Vgg19, and Densenet201. The last-mentioned network has proved to give the best result with 98.34% accuracy of classification dry, wet, and snowy roads.

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Autorzy (2)

Dariusz Grabowski
Andrzej Czyżewski prof. dr hab. inż.

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Wersja publikacji: Accepted albo Published Version
Licencja: otwiera się w nowej karcie

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Kategoria:

Publikacja w czasopiśmie

Typ:

artykuły w czasopismach

Opublikowano w:

JOURNAL OF INTELLIGENT INFORMATION SYSTEMS nr 55, strony 521 - 534,
ISSN: 0925-9902

Język:

angielski

Rok wydania:

2020

Opis bibliograficzny:

Grabowski D., Czyżewski A.: System for monitoring road slippery based on CCTV cameras and convolutional neural networks// JOURNAL OF INTELLIGENT INFORMATION SYSTEMS -Vol. 55, (2020), s.521-534

DOI: