E3S Web Conf.
Volume 164, 2020Topical Problems of Green Architecture, Civil and Environmental Engineering 2019 (TPACEE 2019)
|Number of page(s)||9|
|Section||Energy Efficiency in Transportation|
|Published online||05 May 2020|
Image processing of transport objects using neural networks
1 Bauman Moscow State Technical University (National Research University), 2-nd Baumanskaya, 5, Moscow, 105005, Russia
2 Russian University of Transport (MIIT), Chasovaya str. 22/2, Moscow, 125190, Russia
* Corresponding author: firstname.lastname@example.org
The paper is devoted to the development of an automated system model for monitoring and control of transport objects, based on the processing of images obtained using photo or video detectors, which can be installed on a fixed base near the transport highway for monitoring traffic flows and individual vehicles, and on rolling stock for monitoring transport infrastructure facilities. Image processing occurs by determining the function of blurring the image of an object, algorithms for extracting an image of an object using cascading classifiers and characteristic points, depending on the behavior of the object itself, as well as using a convolutional neural network. Machine learning of the convolutional neural network occurs when using the back propagation method of error. A neural network allows detecting objects of certain classes in the image, determining the parameters of their state and behavior. The proposed model with a movable hardware, which is responsible for obtaining the primary image, was tested on a section of the railway track to identify deviations of the state of the superstructure from the content standards, and a system with stationary photodetectors was tested to determine the parameters of moving vehicles. The obtained results of processing experimental data allowed drawing qualitative conclusions about the possibility of using the proposed algorithms and schemes for monitoring and control of various transport objects.
© The Authors, published by EDP Sciences 2020
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