The goal of this project was to use the histogram of oriented gradients approach and a support vector machine to detect impediments at railroad crossings. A railroad crossing is a location where train tracks cross other routes, such as a highway. Railroad crossings must be equipped with signs, markers, traffic signalling systems, and crossing gate guards, according to Minister of Transportation Regulation Number 36 of 2011. 3477 of the 4716 level crossing points, on the other hand, lack a railroad keeper, making them vulnerable to traffic accidents. Furthermore, at night and in cloudy conditions, hazard information (warning signals) from the railroad keeper to the OOperation Center and machinists can be difficult to view. As a result, the goal of this study is to use the Histogram of Oriented Gradient (HOG) method and the Support Vector Machine (SVM) classifier to detect obstacles (cars) at a railroad crossing. HOG is in charge of extracting object characteristics (cars), whereas SVM is in charge of classifying car objects based on whether or not they meet the criterion for car features. The results show that automobile objects had an accuracy rate of 85 percent, unoccupied train tracks had a rate of 73 percent, and passing trains had a rate of 91 percent.
Author (s) Details
A. Sugiana
School of Electrical Engineering, Telkom University, Bandung, Indonesia.
B. S. Aprillia
School of Electrical Engineering, Telkom University, Bandung, Indonesia.
M. N. Rifqi
School of Electrical Engineering, Telkom University, Bandung, Indonesia.
View Book :- https://stm.bookpi.org/NAER-V8/article/view/2270
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