Abstract
This article is focused on the automatic classification of passing vehicles through an experimental platform using optical sensor arrays. The amount of data generated from various sensor systems is growing proportionally every year. Therefore, it is necessary to look for more progressive solutions to these problems. Methods of implementing artificial intelligence are becoming a new trend in this area. At first, an experimental platform with two separate groups of fiber Bragg grating sensor arrays (horizontally and vertically oriented) installed into the top pavement layers was created. Interrogators were connected to sensor arrays to measure pavement deformation caused by vehicles passing over the pavement. Next, neural networks for visual classification with a closed-circuit television camera to separate vehicles into different classes were used. This classification was used for the verification of measured and analyzed data from sensor arrays. The newly proposed neural network for vehicle classification from the sensor array dataset was created. From the obtained experimental results, it is evident that our proposed neural network was capable of separating trucks from other vehicles, with an accuracy of 94.9%, and classifying vehicles into three different classes, with an accuracy of 70.8%. Based on the experimental results, extending sensor arrays as described in the last part of the paper is recommended.
Highlights
The issue of traffic monitoring and management has arisen due to a growing number of personal vehicles, trucks, and other types of vehicles
The main goal of the research is the use of optical sensor networks for the classification of passing vehicles through a test platform based on neural networks for car recognition using an industrial camera
The test platform for the measurement of additional vehicle characteristics is located at the University of Zilina campus on the entry road to the main parking lot. This monitoring area consists of several sensor arrays based on two technological applications of Fiber Bragg Grating (FBG) sensors
Summary
The issue of traffic monitoring and management has arisen due to a growing number of personal vehicles, trucks, and other types of vehicles. Due to existing road capacities being based on historic designs, the condition of these road communications deteriorates with a lack of growing financial investment to maintain and expand the road network. With these requirements, vehicle visual identification is not sufficient for traffic management and the prediction of the future state of traffic and road conditions. Vehicle visual identification is not sufficient for traffic management and the prediction of the future state of traffic and road conditions For this purpose, existing monitoring areas are being innovated with new sensor platforms, for the statistical purpose of monitoring areas. Each of them has various advantages and disadvantages, such as operating duration, traffic density, meteorological condition limits, resistance to chemical and mechanical damage from maintenance vehicles, etc
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