Lane Detection Method Based on Deformable Linear Convolution

Autonomous driving systems mainly rely on accurate detection of lane markings for navigation and safety. This paper explores an enhanced lane detection methodology employing deformable linear convolution, which dynamically adjusts to geometric variations of road markings. Our method aims to improve the detection fidelity under a range of challenging conditions such as variable illumination, road wear, and diverse weather scenarios, as evidenced by our experiments on the BDD100K dataset. The results demonstrate an improvement over those traditional lane detection techniques, suggesting that the deformable linear convolution offers a viable path forward for complex environmental adaptation in real-time image processing. Nevertheless, the computational demands of the proposed method in this paper highlight an area for further optimization. This study contributes to the field by providing an adaptable framework for the lane detection and sets the stage for future research focused on operational efficiency.