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Title	Proposed Cnn Model For Myanmar Traffic Sign Recognition System
ID_Doc	43505
Authors	Phu K.T.; Premi M.S.G.
Year	2024
Published	Proceedings of the 21st IEEE International Conference on Computer Applications 2024, ICCA 2024
DOI	http://dx.doi.org/10.1109/ICCA62361.2024.10532891
Abstract	Artificial intelligence has involved as main part to construct smart cities and applications for autonomous system. Traffic Sign recognition is one of the important applications that can be used by autonomous cars. Traffic Sign recognition provides the valuable information about road status to drive safety. Although traffic sign cannot be recognized accurately under low-illumination conditions and time of the day. This system proposes enhancement method based on the brightness value of the image to enhance the low-illumination images. The HSV thresholding method is used to identify traffic sing location from the input image and convolution neural network is proposed to recognized the traffic signs. The system produces good results under night time, cloudy, drizzle rain weather. Myanmar Traffic Sign of red, yellow and blue color traffic signs are used in this system. © 2024 IEEE.
Author Keywords	Bi-histogram Equalization; CNN; Myanmar Traffic Sign; Thresholding

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