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

Abstract

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.

Citations

1 3

CrossRef
0

Web of Science
1 4

Scopus

Authors (2)

Dariusz Grabowski
Andrzej Czyżewski prof. dr hab. inż.

Cite as

Full text

download paper

downloaded 141 times

Publication version: Accepted or Published Version
License: open in new tab

Keywords

Details

Category:

Articles

Type:

artykuły w czasopismach

Published in:

JOURNAL OF INTELLIGENT INFORMATION SYSTEMS no. 55, pages 521 - 534,
ISSN: 0925-9902

Language:

English

Publication year:

2020

Bibliographic description:

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: