Multi-Class Road Marker Detection on Rainy Days Using Deep Learning Approach

Lane detection is a critical component of autonomous driving assistance systems (ADASs), playing a pivotal role in ensuring road safety and orderly vehicle movement. In regions like Southeast Asia, characterized by high rainfall, the challenge of detecting lane markers is exacerbated by blurred markings and road surface issues such as potholes. This research addresses the problem of multi-class lane marker detection under rainy conditions, essential for ADASs to maintain safe and compliant vehicle operations. Using a deep learning approach, the proposed model was trained and tested on the Berkeley Video Dataset, incorporating various weather conditions, including rain. The methodology included 150 training epochs executed through Roboflow, with results visualized on the Wandb platform. The model successfully identified five classes of lane markers, namely dashed, single lane, double lane, none, and zebra crossings, demonstrating robust performance in challenging conditions. Evaluation metrics, including train/box_loss and train/cls_loss, showcased significant improvements, with both loss metrics stabilizing below 1.0 after training, indicating accurate bounding box predictions and classification. The findings support advancements in ADASs, enhancing road safety and fostering a more secure and orderly traffic environment during adverse weather.