Md. Rezwanul Haque

Work place: Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh



Research Interests: Image Processing, Image Manipulation, Image Compression, Computer Architecture and Organization, Computer Vision, Computational Learning Theory, Artificial Intelligence, Computer systems and computational processes


Md. Rezwanul Haque was born on July 25, 1996. He is currently pursuing his B.Sc. degree in Computer Science and Engineering at Khulna University of Engineering & Technology, Khulna, Bangladesh. He is currently in his last year of B.Sc. degree. His research interests include efficient computer vision, image processing, machine learning, deep learning, deep reinforcement learning and interested in artificial intelligence.

Author Articles
A Computer Vision based Lane Detection Approach

By Md. Rezwanul Haque Md. Milon Islam Kazi Saeed Alam Hasib Iqbal Md. Ebrahim Shaik

DOI:, 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.

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