Lane Detection Algorithm Based on Haar Feature Based Coupled Cascade Classifier

To improve the accuracy of lane detection in complex environment, The lane detection algorithm based on Haar feature coupled cascade classifier was proposed. The input image was scaled, and the Regions of Interest (ROI) was extracted according to the position of the vanishing line. The Haar feature of the lane was extracted from the ROI, and a cascade Lane classifier was introduced to roughly detect the lane in the ROI. The line segment detector (LSD) method was used to fit roughly detected lanes. The growth strategy and geometric checking were combined to optimize fitting results and complete target detection. The tests were conducted on multiple datasets, and results show compared with current lane detection methods, the proposed algorithm has higher robustness and accuracy, which was up to 96.5%.