ST-LaneNet: Lane Line Detection Method Based on Swin Transformer and LaneNet

Abstract

Abstract The advancement of autonomous driving heavily relies on the ability to accurate lane lines detection. As deep learning and computer vision technologies evolve, a variety of deep learning-based methods for lane line detection have been proposed by researchers in the field. However, owing to the simple appearance of lane lines and the lack of distinctive features, it is easy for other objects with similar local appearances to interfere with the process of detecting lane lines. The precision of lane line detection is limited by the unpredictable quantity and diversity of lane lines. To address the aforementioned challenges, we propose a novel deep learning approach for lane line detection. This method leverages the Swin Transformer in conjunction with LaneNet (called ST-LaneNet). The experience results showed that the true positive detection rate can reach 97.53% for easy lanes and 96.83% for difficult lanes (such as scenes with severe occlusion and extreme lighting conditions), which can better accomplish the objective of detecting lane lines. In 1000 detection samples, the average detection accuracy can reach 97.83%, the average inference time per image can reach 17.8 ms, and the average number of frames per second can reach 64.8 Hz. The programming scripts and associated models for this project can be accessed openly at the following GitHub repository: https://github.com/Duane711/Lane-line-detection-ST-LaneNet .

Keywords

• Autonomous driving
• Lane line detection
• Deep learning
• Swin transformer