System for monitoring road slippery based on CCTV cameras and convolutional neural networks
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System for monitoring road slippery based on CCTV cameras and convolutional neural networks Dariusz Grabowski1 · Andrzej Czy˙zewski1 Received: 10 June 2020 / Revised: 22 August 2020 / Accepted: 24 August 2020 / © The Author(s) 2020
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. Keywords Machine learning · Convolutional neural networks · Transfer learning · Road safety
1 Introduction Proper assessment of the surface condition in terms of its slipperiness is crucial for road safety. For many drivers, this is a difficult task, which affects the problem of adjusting the speed of the vehicle to the conditions. According to a Polish Police report (Budzy´nski and Tubis 2019) from 2018, 24.1% of accidents caused by drivers took place for this very reason. Another research (Rama 2001) has shown that variable message signs based on weather conditions detection (especially in winter) have a positive effect on speed reduction among drivers. On this basis, it is reasonable to create a system that would accurately recognize the condition of the surface based on available data and inform road users about it. Also, it can be concluded that increasing the availability of road state detection technologies by reducing costs should positively affect safety. As described in the literature (Dey et al. 2015), Andrzej Czy˙zewski
[email protected] Dariusz Grabowski [email protected] 1
Multimedia Systems Department, Gda´nsk University of Technology ETI Faculty, Narutowicza 11/12, Gda´nsk, Poland
Journal of Intelligent Information Systems
easy access to such a system can also be a support for emergency management departments and facilitate better decision making. Information about the condition of the road allows emergency personnel to find the fastest route to the incident location. As it is demonstrated in this paper, this aim is possible to achieve by the use of existing infrastructure like roadside CCTV cameras and machine learning. Before the achieved results are discussed an overview of the system including developed intelligent road signs is presented. The data for neural network training were acquired from 27 CCTV cameras installed at the roadsides by the Polish General Directorate for National Roads and Highways (GDDKiA). The place of application of a trained neural network is an intelligent
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