Daytime road marker recognition using grayscale histogram and pixel values


Md Sani, Zamani and Ab. Ghani, Hadhrami and Besar, Rosli and Loi, W. S. (2016) Daytime road marker recognition using grayscale histogram and pixel values. Internetworking Indonesia Journal, 8 (1). p. 1116. ISSN 1942-9703

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Rapid economic and industrial growth has impelled the use of motor vehicle on the roads worldwide. Despite the advancement in road systems that can handle the vehicle upturn, the increase in traffic accidents has yet to be contended. Road markers are very informative to the drivers while driving and different sets of markers are normally used between the highways and the normal road. Significantly, recognizing the correct road marker is highly essential due to the different types of marker (broken lane, continuous lane, double lanes) which can alert the drivers on the condition especially on the non-highway roads to warn the drivers from not to overtaking at the prohibited area. At day time, from morning until evening, the illumination plays major roles where the light from the sun could impact the images captured on the road. The proposed lane marker detection method is a vision system using new algorithm applying the gray level histogram average median in defining the threshold value to counter the illumination issues and the average median pixel count algorithm for the road marker classification process classifying the correct types of marker throughout the day. The algorithm had been tested at 3 different times namely in the morning, afternoon and evening. The best accuracy is in the afternoon at 98% due to excellent illumination condition whereas the accuracy were lower in the morning and evening at 81.13% and 94.84% respectively due to low light condition, effecting the markers illumination on the road.

Item Type: Article
Uncontrolled Keywords: Lane marker, Real-Time, graylevel histogram, pixel count algorithm, illumination
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 10 Jul 2020 04:30
Last Modified: 21 Dec 2020 05:54


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