Improved Pixel-Based Lane Extraction Algorithm Based on Threshold Segmentation Optimization for Closed Scenes

Considering the current development of science and technology, closed scenes such as factories, airports and warehouses are most suitable for applications and promotions of autonomous vehicles. Due to the high performance-cost ratio of monocular cameras, the concept of vision-based lane recognition has been widely used. The existing lane line extraction algorithms based on vision mainly include region extraction, threshold segmentation, lane line fitting and target point extraction. In the verification process, the threshold segmentation part is seriously affected by the surrounding environment because of its poor robustness; and the failure of lane line fitting will affect the subsequent tracking algorithm. To solve these problems, this paper proposes an Improved Pixel-based Lane Extraction (IPLE) algorithm integrating with the closed scenes’ characteristics. Firstly, the original image is converted into an aerial view by perspective transformation that can form equal-width lane lines in the image and restore the real scene. Secondly, the process of unifying the road and surrounding environments is integrated based on traditional OTSU threshold segmentation to achieve better lane extraction. Thirdly, the pixel distribution is simplified based on clustering method using distance calculation. Finally, the target point extraction is achieved based on pixel coordinates. Compared with the existing lane line fitting algorithm, IPLE algorithm is able to solve the problem of large curvature fitting failures with higher computational efficiency and stronger robustness.