Robust Lane Detection Method Based on Dynamic Region of Interest

Lane detection is an important component of vehicle-assisted driving systems. Many lane line algorithms have been proposed, but lane line detection is still a challenging task in environments with road text or vehicle interference. To address these issues, this article proposes a robust lane line detection method under structured roads. First, we propose a method based on the slope and length of a straight line and the distance of the line from the center line to extract regions of interest reducing interference from other invalid regions. Afterward, we use a new fusion method to fuse the extracted color features with the gradient features, enhancing the lane features while greatly reducing the interference in the pixels. Next, we cut the extracted pixel feature maps into equal parts into several sub-maps and perform pixel statistics for each sub-map, replacing sub-maps with abnormal pixel values with adjacent sub-maps, which can effectively reduce noise. Finally, we use density-based spatial clustering of applications with noise (DBSCAN) to cluster the pixels and fit the lane lines with least squares. The experimental results show that the method can effectively detect lane lines and has good robustness.