Study of methods of segmentation and detection of objects in the image in real time to prevent accidents of Russian Railways
Journal Title: Modern Innovations, Systems and Technologies - Year 2022, Vol 2, Issue 3
Abstract
With the development of the railway industry, the informatization of society and the automation of many technological processes, it becomes possible to create a complexof automatic control, diagnostics and safety of locomotive traffic. One of the most important systems of this complex is the system for detecting objects on the railway tracks, breaks in the railway track and its turns. Such a system can be designed as a locomotive-mounted camera and information processing systems on board each rolling stock, or as a global system that performs remote processing of information from several locomotives. Regardless of the implementation of the system, there is a need to create a block for detecting objects in images coming from cameras. To implement this block, it is necessary to select a railway lane in the image and detect objects in real time. Segmentation methods are used to select a band. The article presents the algorithms of several of them and chooses the most preferred option. The task of detecting objects in a video stream in real time is solved using convolutional neural networks. The article provides brief descriptions of several networks, analyzes the results of the described neural networks, and selects the network that is most suitable for solving the problem
Authors and Affiliations
A. T. Tisetsky, D. I. Kovalev, T. P. Mansurova
Keywords
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- EP ID EP708580
- DOI https://doi.org/10.47813/2782-2818-2022-2-3-0101-0116
- Views 131
- Downloads 0
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