LanePainter: Lane Marks Enhancement via Generative Adversarial Network

For safety purposes, understanding the quality of the lane marks is essential for advanced driving technologies and pavement road maintenance. In practice, the performance of existing lane detection algorithms [1]–[3] struggles with lane marks in poor conditions, especially in rural areas where the lane marks are less maintained. However, enhancing the quality of lane marks is still not thoroughly studied. In this research, we collected a new public dataset to benchmark the performance of lane detection algorithms on various lane marks conditions from different rural areas in the U.S. Also, this paper proposed LanePainter, a Generative Adversarial Network (GAN) based model, which simultaneously enhances and assesses lane marks quality in street view images. To increase the detectability of lane marks, LanePainter enhances degraded lane marks of street view images directly on image pixels without affecting the high-quality lane marks. Our experiments demonstrated a substantial increase in the performance of current lane detection algorithms on low-quality lane marks after enhancing the images using our proposed LanePainter algorithm. Also, pavement maintenance can take benefit from our classification assessment method to quickly and accurately locate the regions with low-quality lane marks.