Lane Detection for Autonomous Vehicles using Image Transformation Techniques

Lane detection is critical in autonomous driving and advanced driver assistance systems (ADAS), furnishing vital information for vehicle navigation and safety. The study introduces lane detection methodology leveraging established image processing techniques within the Image transformation frameworks, including the study of kernels. This approach accurately detects lane markings in real-time images or video streams with the help of Gaussian blur and Canny edge detection. The system's evaluation primarily focuses on structured roads under standard conditions, demonstrating its efficacy in such environments. However, the potential for enhancing vehicle autonomy and safety across varied driving scenarios remains prominent. Leveraging Image transformation capabilities and the insights gained from kernel studies, this research advances computer vision applications in the automotive sector, facilitating the evolution of more intelligent and adaptable driving systems. Furthermore, the study introduces a method for assessing the accuracy of the detected lanes by calculating the intersection over union (IOU).