Lane marking detection based on vertical noise mitigation modules
Accurate detection of lane markings is one of the primary tasks for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. State-of-the-art lane marking detection algorithms employ object-detection methods based on Convolutional Neural Networks (CNN) and have been widely recognised for yielding accurate detection performance so far. However, pitfalls abound, especially under adverse lighting conditions. Glaring lights or starbursts can create geometric patterns that resemble lane markings, thereby leading to inaccurate detection. To address these challenges, this study presents a novel Vertical Noise Mitigation Module (VNMM) embedded in a hybrid end-to-end CNN architecture that minimises noise interference in the image, thereby increasing the robustness of lane marking detection. VNMM reduces image-related noise and distortion to improve the detection accuracy under adverse lighting conditions. It approaches the problem in a column-oriented classification manner and extracts the features column-wise, since lane markings predominantly appear vertically in an image. The VNMM reduces lateral interference spatially by using channel dimensions to capture noise that is oriented diagonally or laterally. The proposed method is evaluated using a dataset that was specifically collected for this purpose, as well as two widely recognised lane marking datasets: CULane and TuSimple. The method’s effectiveness under adverse lighting conditions is highlighted by the improved accuracy compared with other state-of-the-art techniques in the experimental results. Despite the challenging scenes within the CULane dataset, particularly in crowded scenarios, this approach yielded a lane marking detection accuracy of 81.2%, compared with the previously reported accuracy of 80.6%. Thus, this study contributes significantly to the field of lane marking detection, especially under adverse lighting conditions.