Work place: Department of Civil Engineering, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh
Research Interests: Engineering, Computational Engineering, Computational Science and Engineering
Md. Ebrahim Shaik was born in 1995 and obtained his B.Sc. degree in Civil Engineering from the Khulna University of Engineering & Technology, Khulna, Bangladesh in 2017. Now he is pursuing his M.Sc. in Civil Engineering in that university with the major transportation engineering. He is currently a lecturer & coordinator of Civil Engineering department at the Northern University of Business and Technology, Khulna. During his undergraduate studies, he wrote reports on several Civil Engineering projects including transportation. He also achieved the best paper award in transportation engineering for the paper titled as ‘An Artificial Neural Network Model for Road Accident Prediction: A Case Study of Khulna Metropolitan city’, Khulna, published in International Conference on Civil Engineering for Sustainable Development (ICCESD 2018) organized by the Department of Civil Engineering, Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh
DOI: https://doi.org/10.5815/ijigsp.2019.03.04, Pub. Date: 8 Mar. 2019
Automatic lane detection to help the driver is an issue considered for the advancement of Advanced Driver Assistance Systems (ADAS) and a high level of application frameworks because of its importance in drivers and passerby safety in vehicular streets. But still, now it is a most challenging problem because of some factors that are faced by lane detection systems like as vagueness of lane patterns, perspective consequence, low visibility of the lane lines, shadows, incomplete occlusions, brightness and light reflection. The proposed system detects the lane boundary lines using computer vision-based technologies. In this paper, we introduced a system that can efficiently identify the lane lines on the smooth road surface. Gradient and HLS thresholding are the central part to detect the lane lines. We have applied the Gradient and HLS thresholding to identify the lane line in binary images. The color lane is estimated by a sliding window search technique that visualizes the lanes. The performance of the proposed system is evaluated on the KITTI road dataset. The experimental results show that our proposed method detects the lane on the road surface accurately in several brightness conditions.[...] Read more.
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