Lane detection and departure warning systems play a crucial role in ensuring driver safety and preventing accidents on roadways. In this paper, we propose a comprehensive approach for lane detection and departure using image processing techniques. Our methodology involves a series of steps, beginning with color thresholding to isolate relevant lane markings from the surrounding environment. Subsequently, a region of interest (ROI) is selected to focus the analysis on the relevant portion of the image, reducing computational complexity and improving accuracy. Canny edge detection is then employed to identify edges within the ROI, providing a more precise representation of lane boundaries. Finally, the Hough line transform is applied to detect and extract straight lines corresponding to lane markings from the edge-detected image. We conducted experiments on various realworld road scenarios to evaluate the performance of our proposed approach. The results demonstrate the effectiveness of our method in accurately detecting lane markings and providing timely departure warnings. Our approach exhibits robustness against variations in road surface textures, and lane markings of different shapes and sizes. Furthermore, the computational efficiency of our method makes it suitable for real-time implementation in onboard vehicle systems. Overall, our research contributes to the advancement of intelligent transportation systems by offering a reliable and efficient solution for lane detection and departure warning, ultimately enhancing road safety for drivers and passengers alike.
Overview of Canny Edge Detection and Hough Transform for Lane Detection
2024-06-05
386857 byte
Conference paper
Electronic Resource
English
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